Biology subjects

Faryabi, R.

Publications and source records attributed to Faryabi, R..

5 recordsLinked to original sources

bioRxiv2025

Single-Allele Chromatin Tracing Reveals Genomic Clustering of Paralogous Transcription Factors as a Mechanism for Developmental Robustness in T Cells

In metazoans, gene duplication has given rise to paralogous transcription factors, which have functionally diversified to control cellular differentiation. While the majority of paralogous TFs are dispersed across different chromosomes, some remain clustered raising the question of whether genomic proximity confers any evolutionary advantage for TF clusters. To address this, we investigated a [~]1 Mbp locus containing two ETS family paralogs, Ets1 and Fli1. Using a sub-diffraction sequential imaging technique called Optical Reconstruction of Chromatin Architecture (ORCA), we traced the 3D organization of this region in single alleles of T cells from genetically engineered mice with targeted deletions of key regulatory elements. In wild-type T cells, the predominant chromatin conformation spatially links Ets1 to its proximal super-enhancer, segregating Ets1 from Fli1. This topology correlates with high Ets1 and low Fli1 expression. Deletion of the Ets1 super-enhancer abolishes this configuration, triggering locus-wide architectural rewiring that increases Ets1-Fli1 promoter-promoter interactions and subsequently the co-expression of two genes within individual cells. Remarkably, this compensatory interaction bypasses insulated chromatin domains, sustaining Ets1 levels necessary for T cell development despite enhancer loss. Our results reveal that genomic clustering of TF paralogs enables dynamic architectural plasticity: while a super-enhancer fine-tunes paralog expression balance in wild-type contexts, its deletion unmasks latent promoter-driven coordination, suggesting that proximity safeguards functional redundancy and transcriptional resilience critical for cellular fitness.

genomics↗

bioRxiv2023

Modeling Type 1 Diabetes progression from single-cell transcriptomic measurements in human islets

Type 1 diabetes (T1D) is a chronic condition in which the insulin-producing beta cells are destroyed by immune cells. Research in the past few decades characterized the immune cells involved in disease pathogenesis and has led to the development of immunotherapies that can delay the onset of T1D by two years. Despite this progress, early detection of autoimmunity in individuals who will develop T1D remains a challenge. Here, we evaluated the potential of combining single-cell genomics and machine learning strategies as a prime approach to tackle this challenge. We used gradient-boosting-based machine learning algorithms and modeled changes in transcriptional profiles of single cells from pancreatic tissues in T1D and nondiabetic organ donors collected by the Human Pancreas Analysis Program. We assessed whether mathematical modelling could predict the likelihood of T1D development in nondiabetic autoantibody-positive organ donors. While the majority of autoantibody-positive organ donors were predicted to be nondiabetic by our model, select donors with unique gene signatures were classified with the T1D group. Remarkably, our strategy also revealed a shared gene signature in distinct T1D associated models based on different cell types including alpha cells, beta cells and acinar cells, suggesting a common effect of the disease on transcriptional outputs of these cells. Together, our strategy presents the first report on the utility of machine learning algorithms in early detection of molecular changes in T1D.

genomics↗

bioRxiv2023

Computational workflow and interactive analysis of single-cell expression profiling of islets generated by the Human Pancreas Analysis Program

Type 1 and Type 2 diabetes are distinct genetic diseases of the pancreas which are defined by the abnormal level of blood glucose. Understanding the initial molecular perturbations that occur during the pathogenesis of diabetes is of critical importance in understanding these disorders. The inability to biopsy the human pancreas of living donors hampers insights into early detection, as the majority of diabetes studies have been performed on peripheral leukocytes from the blood, which is not the site of pathogenesis. Therefore, efforts have been made by various teams including the Human Pancreas Analysis Program (HPAP) to collect pancreatic tissues from deceased organ donors with different clinical phenotypes. HPAP is designed to define the molecular pathogenesis of islet dysfunction by generating detailed datasets of functional, cellular, and molecular information in pancreatic tissues of clinically well-defined organ donors with Type 1 and Type 2 diabetes. Moreover, data generated by HPAP continously become available through a centralized database, PANC-DB, thus enabling the diabetes research community to access these multi-dimensional data prepublication. Here, we present the computational workflow for single-cell RNA-seq data analysis of 258,379 high-quality cells from the pancreatic islets of 67 human donors generated by HPAP, the largest existing scRNA-seq dataset of human pancreatic tissues. We report various computational steps including preprocessing, doublet removal, clustering and cell type annotation across single-cell RNA-seq data from islets of four distintct classes of organ donors, i.e. non-diabetic control, autoantibody positive but normoglycemic, Type 1 diabetic, and Type 2 diabetic individuals. Moreover, we present an interactive tool, called CellxGene developed by the Chan Zuckerberg initiative, to navigate these high-dimensional datasets. Our data and interactive tools provide a reliable reference for singlecell pancreatic islet biology studies, especially diabetes-related conditions.

genomics↗

bioRxiv2020

Tyrosine phosphorylation of ACLY regulates lipid metabolism and oncogenesis in ALK positive Anaplastic Large Cell Lymphoma

A fundamental requirement for growth of rapidly proliferating cells is metabolic adaptation to promote synthesis of biomass1. ATP citrate lyase (ACLY) is a critical enzyme responsible for synthesis of cytosolic acetyl-CoA, the key building component for de novo fatty acid synthesis and links vital pathways such as carbohydrate and lipid metabolism2. The mechanisms of ACLY regulation are not completely understood and the regulation of ACLY function by tyrosine phosphorylation is unknown. Here we show using mass-spectrometry-driven phosphoproteomics and metabolomics that ACLY is phosphorylated and functionally regulated at an evolutionary conserved residue, Y682. Physiologic signals promoting rapid cell growth such as epidermal growth factor stimulation in epithelial cells and T-cell receptor activation in primary human T-cells result in rapid phosphorylation of ACLY at Y682. In vitro kinase assays demonstrate that Y682 is directly phosphorylated by multiple tyrosine kinases, including ALK, ROS1, SRC, JAK2 and LTK. Oncogenically activating structural alterations such as gene-fusions, amplification or point mutations of ALK tyrosine kinase result in constitutive phosphorylation of ACLY in diverse forms of primary human cancer such as lung cancer, anaplastic large cell lymphoma (ALCL) and neuroblastoma. Expression of a phosphorylation-defective ACLY-Y682F mutant in NPM-ALK+ ALCL decreases ACLY activity and attenuates lipid synthesis. Metabolomic analyses reveal that ACLY-Y682F expression results in increased {beta}-oxidation of 13C-oleic acid-labeled fatty acid with increased labeling of +2-citrate (p<0.01) and +18-oleyol carnitine (p<0.001). Similarly, oxygen consumption rate (OCR) is significantly increased in cells expressing ACLY-Y682F (p<0.001). Moreover, expression of ACLY-Y682F dramatically decreases cell proliferation, impairs clonogenicity and abrogates tumor growth in vivo. Our results reveal a novel mechanism for direct ACLY regulation that is subverted by multiple oncogenically-activated tyrosine kinases in diverse human cancers. These findings have significant implications for novel therapies targeting ACLY in cancer and metabolism.

cancer biology↗

bioRxiv2018

Classes of ITD predict outcomes in patients with AML treated with FLT3 inhibitors

Recurrent internal tandem duplication (ITD) mutations are observed in various cancers including acute myeloid leukemia (AML). ITD mutations of Fms-like tyrosine kinase 3 (FLT3) receptor increase kinase activity, and are associated with poor prognostic outcomes. Currently, several small-molecule FLT3 inhibitors (FLT3i) are in clinical trials for targeted therapy of high-risk FLT3-ITD-positive AML. However, the variability of survival following FLT3i treatment suggests that the mere presence of FLT3-ITD mutations in a patient might not guarantee effective clinical response to targeted inhibition of FLT3 kinase. Motivated by the heterogeneity of FLT3-ITD mutations, we sought to investigate the effects of FLT3-ITD structural features on response to treatment in AML patients. To this end, we developed HeatITup (HEAT diffusion for Internal Tandem dUPlication), an algorithm to efficiently and accurately identify ITDs and classify them based on their nucleotide composition into newly defined categories of \"typical\" or \"atypical\". Typical ITDs insert sequences are entirely endogenous to the FLT3 locus whereas atypical ITDs contain nucleotides exogenous to the wildtype FLT3. We applied HeatITup to our cohort of de novo and relapsed AML patients. Individuals with AML carrying typical ITDs benefited significantly more from FLT3i than patients with atypical ITDs, regardless of whether FLT3i was used after initial induction or at relapse. Furthermore, analysis of the TCGA AML cohort demonstrated improved survival for patients with typical ITDs treated with induction chemotherapy. These results underscore the importance of structural discernment of complex somatic mutations such as ITDs in progressing towards personalized treatment for AML patients.

cancer biology↗