Biology subjects

Faryabi, R. B.

Publications and source records attributed to Faryabi, R. B..

12 recordsLinked to original sources

bioRxiv2025

Lineage-determining transcription factors constrain cohesin to drive multi-enhancer oncogene regulation

Multiple enhancers, often located across vast genomic distances, regulate key genes. However, how chromatin topology organization at individual alleles enables cell-type-restricted multi-enhancer gene regulation remains unclear. Using acute protein degradation and time-course population-average chromatin conformation capture in lymphoma, we found that the B-cell- lineage-determining transcription factor EBF1 preferentially positions multiple enhancers at loci containing sparsely distributed genes essential for B-cell identity and oncogenesis. Our time-resolved sub-diffraction optical chromatin architecture tracing of >100,000 alleles in individual lymphoma cells further revealed diverse topological conformations facilitating multi-enhancer interactions. Mechanistically, we found that positioning of enhancers at allelic topological centers is required for their interactions with target promoters, with EBF1 serving as a barrier to the loop-extruding cohesin on enhancers. These findings, which we demonstrate their generalizability to the T-cell-lineage-determining transcription factor TCF1 in T-cell leukemia, suggest that lineage-determining transcription factors radially position enhancers and promoters to enable multi-enhancer regulation of key oncogenes.

cancer biology↗

bioRxiv2025

OLIVE Provides Rapid Visualization and Analysis of Chromatin Tracing Experiments

Optical chromatin tracing experiments directly capture the three-dimensional folding of thousands of individual alleles, highlighting the need for a tool that enables fast, interactive, and analytical browsing of such data. Here, we introduce Optical Looping Interactive Viewing Engine (OLIVE), a web-based tool designed for high-throughput chromatin tracing data that functions similarly to genome browsers. OLIVE allows users, regardless of computational expertise, to input their own data for automated reconstruction of chromatin fibers at individual alleles or to browse annotated publicly available datasets. Using OLIVEs functionalities, users can interact with three-dimensional presentation of traced alleles and query them based on spatial features, including pairwise distances and perimeters between their segments. Finally, OLIVE calculates and presents several polymer physics metrics of each allele, providing quantitative summaries for hypothesis-driven studies. OLIVE is an open-source project accessible at https://faryabilab.github.io/chromatin-traces-vis/.

bioinformatics↗

bioRxiv2025

Azurify integrates cancer genomics with machine learning to classify the clinical significance of somatic variants

Accurate classification of somatic variations from high-throughput sequencing data has become integral to diagnostics and prognostics across various cancers. However, the classification of these variations remains highly manual, inherently variable, and largely inaccessible outside specialized laboratories. Here, we introduce Azurify - a computational tool that integrates machine learning, public resources recommended by professional societies, and clinically annotated data to classify the pathogenicity of variations in precision cancer medicine. Trained on over 15,000 clinically classified variants from 8,202 patients across 138 cancer phenotypes, Azurify achieves 99.1% classification accuracy for concordant pathogenic variants in data from two external clinical laboratories. Additionally, Azurify reliably performs precise molecular profiling in leukemia cases. Azurifys unified, scalable, and modular framework can be easily deployed within bioinformatics pipelines and retrained as new data emerges. In addition to supporting clinical workflows, Azurify offers a high-throughput screening solution for research, enabling genomic studies to identify meaningful variant-disease associations with greater efficiency and consistency.

genomics↗

bioRxiv2024

Enhancer-promoter hubs organize transcriptional networks promoting oncogenesis and drug resistance

Recent advances in high-resolution mapping of spatial interactions among regulatory elements support the existence of complex topological assemblies of enhancers and promoters known as enhancer-promoter hubs or cliques. Yet, organization principles of these multi-interacting enhancer-promoter hubs and their potential role in regulating gene expression in cancer remains unclear. Here, we systematically identified enhancer-promoter hubs in breast cancer, lymphoma, and leukemia. We found that highly interacting enhancer-promoter hubs form at key oncogenes and lineage-associated transcription factors potentially promoting oncogenesis of these diverse cancer types. Genomic and optical mapping of interactions among enhancer and promoter elements further showed that topological alterations in hubs coincide with transcriptional changes underlying acquired resistance to targeted therapy in T cell leukemia and B cell lymphoma. Together, our findings suggest that enhancer-promoter hubs are dynamic and heterogeneous topological assemblies with the potential to control gene expression circuits promoting oncogenesis and drug resistance.

molecular biology↗

bioRxiv2024

Aberrant TNF signaling in pancreatic lymph nodes of patients with Type 1 Diabetes

The therapeutic landscape for Type 1 Diabetes (T1D) is rapidly changing as ongoing clinical trials aim to delay beta-cell loss by inhibiting proinflammatory cytokines. However, the precise timing and cellular contexts of cytokine dysregulation remains unknown. We generated the largest existing measurement of gene expression and chromatin accessibility in [~]1 million immune cells from the pancreatic lymph nodes and spleens of 34 T1D and non-diabetic organ donors. Our study revealed heightened gene activity of the tumor necrosis factor (TNF) pathway and subsequent chromatin remodeling in central memory CD4+ T cells residing in the pancreatic lymph nodes of T1D and non-diabetic islet-autoantibody positive donors. These findings, validated in mice, offer a mechanism underlying the efficacy of TNF inhibitors, currently undergoing clinical trials to delay T1D onset.

immunology↗

bioRxiv2023

The pseudokinase Trib1 regulates the transition of exhausted T cells to a KLR+ CD8+ effector state and its deletion improves checkpoint blockade

T cell exhaustion (TEX) impairs the ability of T cells to clear chronic infection or cancer. While exhausted T cells are hypofunctional, some exhausted T cells retain effector gene signatures, a feature that is associated with expression of KLRs (killer lectin-like receptors). Although KLR+ T cells may improve control of chronic antigen, the signaling molecules regulating this population are poorly understood. Using scRNA-seq, flow cytometry, RNA velocity, and scTCR-seq, we demonstrate that deleting the pseudokinase Trib1 shifts TEX towards CX3CR1+ intermediates (TINT) with robust enrichment of KLR+CD8+ T cells (TKLR) via clonal T cell expansion. These changes are associated with globally increased KLR gene expression throughout the exhaustion program. Further, Trib1 loss augments anti-PD-L1 blockade to improve viral clearance by expanding the TKLR population. Together, these data identify Trib1 as an important regulator of T cell exhaustion whose targeting enhances the KLR+ effector state and improves the response to checkpoint inhibitor therapy.

immunology↗

bioRxiv2023

AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics

Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs, we developed AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX show the superior performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulated known islet pathobiology and showed differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8+ T cells infiltration in islets during type 1 diabetes progression.

bioinformatics↗

bioRxiv2021

Multiomics single-cell analysis of human pancreatic islets reveals novel cellular states in health and type 1 diabetes

Type 1 diabetes (T1D) is an autoimmune disease of only partially defined etiology in which immune cells destroy insulin-producing beta cells. Using single-cell transcriptomics and an advanced analytical strategy to assess pancreatic islets of T1D, autoantibody-positive, and non-diabetic organ donors, we identified both canonical cell types and rare insulin-expressing cells with a hybrid mixture of endocrine and exocrine gene signatures within all donors. We further found elevated expression of MHC Class II pathway genes in exocrine ductal cells of T1D donors, which we confirmed through CyTOF, in situ imaging mass cytometry, and immunofluorescence analysis. Taken together, our multimodal analyses identify novel cell types and processes that may contribute to T1D immunopathogenesis and provide new cellular and molecular insights into human pancreas function.

genomics↗

bioRxiv2020

ALC1 links chromatin accessibility to PARP inhibitor response in homologous recombination deficient cells

The response to Poly (ADP-ribose) polymerase inhibitors (PARPi) is dictated by homologous recombination (HR) DNA repair mechanisms and the abundance of lesions that trap PARP enzymes on chromatin. It remains unclear, however, if the established role of PARP in promoting chromatin accessibility impacts viability in these settings. Using a CRISPR based screen, we identify the PAR-binding Snf2-like ATPase, ALC1/CHD1L, as a key determinant of PARPi toxicity in HR-deficient cells. ALC1 loss reduced viability of BRCA mutant cells and enhanced their sensitivity to PARPi by up to 250-fold, while overcoming several known resistance mechanisms. ALC1 loss was not epistatic to other repair pathways that execute the PARPi response. Instead, ALC1 deficiency reduced chromatin accessibility concomitant with a decrease in the association of repair factors. This resulted in an accumulation of replication associated DNA damage and a reliance on HR. These findings establish PAR-dependent chromatin remodeling as a mechanistically distinct aspect of PARPi responses, implicating ALC1 inhibition as a new approach to overcome therapeutic resistance in HR-deficient cancers.

cancer biology↗

bioRxiv2019

Oncogenic Notch promotes long-range regulatory interactions within hyperconnected 3D cliques

Chromatin loops enable transcription factor-bound distal enhancers to interact with their target promoters to regulate transcriptional programs. Although developmental transcription factors, such as active forms of Notch, can directly stimulate transcription by activating enhancers, the effect of their oncogenic subversion on the 3-dimensional (3D) organization of the cancer genome is largely undetermined. By mapping chromatin looping genome-wide in Notch-dependent triple-negative breast cancer and B-cell lymphoma, we show that far beyond the well-characterized role of Notch as an activator of distal enhancers, Notch regulates its direct target genes through establishing new long-range regulatory interactions. Moreover, a large fraction of Notch-promoted regulatory loops forms highly interacting enhancer and promoter spatial clusters, termed "3D cliques". Loss-and gain-of-function experiments show that Notch preferentially targets hyperconnected 3D cliques that regulate the expression of crucial proto-oncogenes. Our observations suggest that oncogenic hijacking of developmental transcription factors can dysregulate transcription through widespread effects on the spatial organization of cancer genomes.

cancer biology↗

bioRxiv2019

TooManyCells identifies and visualizes relationships of single-cell clades

Transcriptional programs contribute to phenotypic and functional cell states. While elucidation of cell state heterogeneity and its role in biology and pathobiology has been advanced by studying single cell level measurements, the underlying assumptions of current analytical methods limit the identification and exploration of cell clades. Unlike other methods, which produce a single uni-layer partition of cells ignoring echelons of cell states, we present TooManyCells, a software consisting of a suite of graph-based tools for efficient, global, and unbiased identification and visualization of cell clades while maintaining and presenting the relationship between cell states. TooManyCells provides a set of tools based on a matrix-free efficient divisive hierarchical spectral clustering algorithm wholly different from the prevalent Louvain-based methods. BirchBeer, the visualization component of TooManyCells, introduces a new approach for single cell analysis that is built on a concept intentionally orthogonal to the widely used dimensionality reduction methods. Together, this suite of tools provide a paradigm shift in the analysis and interpretation of single cell data by enabling simultaneous comparisons of cell states at context-and application-dependent scales. A byproduct of this shift is the immediate detection and visualization of rare populations that outperforms previous algorithms as demonstrated by applying these tools to existing single cell RNA-seq data sets from various mouse organs.

bioinformatics↗

bioRxiv2018

Differential Integration of Transcriptome and Proteome Identifies Pan-cancer Prognostic Biomarkers

High-throughput analysis of the transcriptome and proteome individually are used to interrogate complex oncogenic processes in cancer. However, an outstanding challenge is how to combine these complementary, yet partially disparate data sources to accurately identify tumor-specific gene-programs and clinical biomarkers. Here, we introduce inteGREAT for robust and scalable differential integration of high-throughput measurements. With inteGREAT, each data source is represented as a co-expression network, which is analyzed to characterize the local and global structure of each node across networks. inteGREAT scores the degree by which the topology of each gene in both transcriptome and proteome networks are conserved within a tumor type, yet different from other normal or malignant cells. We demonstrated the high performance of inteGREAT based on several analyses: deconvolving synthetic networks, rediscovering known diagnostic biomarkers, establishing relationships between tumor lineages, and elucidating putative prognostic biomarkers which we experimentally validated. Furthermore, we introduce the application of a clumpiness measure to quantitatively describe tumor lineage similarity. Together, inteGREAT not only infers functional and clinical insights from the integration of transcriptomic and proteomic data sources in cancer, but also can be readily applied to other heterogeneous high-throughput data sources. inteGREAT is open source and available to download from https://github.com/faryabib/inteGREAT.

bioinformatics↗