Biology subjects

Zarrineh, P.

Publications and source records attributed to Zarrineh, P..

8 recordsLinked to original sources

bioRxiv2025

Subtype-specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

Gene expression is tightly controlled by DNA elements called enhancers by associating with lineage-specific transcription factors. These enhancers transcribe non-coding RNAs (called enhancer RNAs or eRNAs). eRNA expression is an early indicator of transcription factor activity and is associated with treatment response and survival in cancer patients. However, the attempts to identify prognostic eRNAs in breast cancers were inadequate, as these studies ignored the heterogenous nature of breast cancers with distinct molecular subtypes. By analysing [~]300,000 eRNA loci profiled using RNA-sequencing datasets from 1,095 breast cancer patients using machine learning approaches, we categorised eRNAs which are specific to breast cancer molecular subtypes and survival. The classified eRNAs were associated with gene pathways related to relevant subtypes. Interestingly, transcription factor analyses highlighted involvement of nuclear receptors other than the estrogen receptor with luminal-specific eRNAs. Basal eRNAs showed association with the transcriptional corepressor TRIM28 and androgen receptor. Luminal eRNAs were associated with better outcomes and Her2 eRNAs with worse outcome in patients. Overall, we demonstrate that machine learning approaches performed on RNA-seq datasets can classify subtype-specific and prognostic eRNAs which can be used to identify critical gene pathways and transcription factor networks in breast cancer.

cancer biology↗

bioRxiv2024

The evolutionary ancient MEIS transcription factors actuate lineage-specific transcription to establish cardiac fate

Control of gene expression is commonly mediated by distinct combinations of transcription factors (TFs). This cooperative action allows multiple biological signals to be integrated at specific regulatory elements, resulting in highly specific gene expression patterns in space and time. It is unclear whether combinatorial binding is also necessary to bring together TFs with distinct biochemical functions, which collaborate to effectively recruit and activate RNA polymerase II. Using a cardiac differentiation model, we find that the largely ubiquitous, evolutionary ancient homeodomain proteins MEIS are essential for activating a cardiac-specific gene expression program. MEIS TFs act as actuators, fully activating transcriptional programs selected by lineage-restricted TFs to drive the dynamic progression of cardiac differentiation. Combinatorial binding of MEIS with lineage-enriched TFs, GATA and HOX, provides selectivity, guiding MEIS to function at cardiac-specific enhancers. In turn, MEIS TFs promote accumulation of the methyltransferase KMT2D to initiate lineage-specific enhancer commissioning. MEIS combinatorial binding dynamics, dictated by the changing dosage of its partners, drive cells into progressive stages of cardiac differentiation. Our results uncover tissue-specific transcriptional activation as the result of ubiquitous actuator TFs harnessing general transcriptional coactivators at tissue-specific enhancers, to which they are directed by binding with lineage- and domain-specific TFs.

developmental biology↗

bioRxiv2023

A pipeline to identify TF combinatorial binding uncovers TEAD1 as an antagonist of tissue-specific transcription factors in human organogenesis

Gene expression is largely controlled by transcription factors (TFs), which bind to enhancers in combination with other TFs in a mechanism known as combinatorial binding. While combinatorial binding is well established, a comprehensive view of tissue-specific TF combinations at active enhancers during human embryonic development is still lacking. Using a two-step pipeline to detect co-occurring TF motifs in developmental enhancers across 11 human embryonic tissues, we found that motifs recognized by ubiquitous TF families, including TEAD, TALE, ETS, and STAT, are enriched near tissue-specific sequence signatures in developmental enhancers across multiple tissues. In human heart enhancers, TEAD and GATA motifs frequently co-occur, and in the developing mouse heart TEAD1 and GATA4 co-occupy a set of genomic regions, which are also preferentially bound by CHD4, a component of the NuRD complex involved in transcriptional repression. Consistently, TEAD1 attenuates enhancer activation in vitro, with this repressive effect dependent on tissue-specific activators. Overall, our findings reveal universal patterns of TF connectivity within organ-specific transcriptional networks and highlight a broad, previously unrecognized role for TEAD in coordinating organ growth and differentiation across multiple tissues.

genomics↗

bioRxiv2022

Motif2Site: a Bioconductor package to detect accurate transcription factor binding sites from ChIP-seq

AO_SCPLOWBSTRACTC_SCPLOWTranscription factors (TFs) binding are in the core of the Regulatory networks studies. ChIP-seq experiments are available for many TFs in various species. As TFs co-bind in cis-regulatory elements regions to control gene expression, studying the existing relation among co-binding TFs such as distance of binding sites or co-occupancy are highly important to understand the regulatory mechanisms. Currently, to detect binding sites of each TF in cis-regulatory elements, first binding regions of each TF are detected by standard peak calling methods, and at the second step the best candidate binding sites are prioritized by motif detection methods in binding regions. However, it is well-known that the best prioritized candidate motifs are not necessarily the actual binding sites of TFs. Furthermore, motif prioritizing methods that consider more genomic features complexities of TFs bindings are usually computationally expensive methods. Here, we tend to improve the TF binding sites accuracy detection by using the original ChIP-seq signal. The motifs which are located closer to the summits of binding region peaks are more likely to be the actual binding sites. Therefore, We developed a novel post-processing Bioconductor package called Motif2Site to detect TFs binding sites from user provided motif sets and recenter them across experiments. We applied Motif2Site method to detect TF binding sites for major mouse embryonic stem cell (mESC) as well as mouse fetal and birth time (P0) heart TFs. Motif2Site could detect binding regions with comparable accuracy to the existing state-of-the-art while it substantially increased the accuracy of the detected binding sites. Motif2Site could future improve the accuracy of binding sites prediction by recentering binding sites across developmental conditions (fetal/P0 heart) and across homologous TFs (ex. GATA4/GATA6 and MEF2A/MEF2C). Purifying high-confidence binding sites in mouse fetal heart, enabled us to study the co-binding properties of TFs in cis-regulatory elements. We could also traced TFs footprints in selected heart-specific VISTA enhancers chromatin accessible regions.

bioinformatics↗

bioRxiv2022

A spatio-temporally constrained gene regulatory network directed by PBX1/2 acquires limb patterning specificity via HAND2

During development cell fates are specified by tightly controlled gene expression programs. PBX TALE transcription factors control gene regulatory networks (GRN) that direct vertebrate tissue patterning and organ morphogenesis. How PBX1/2 proteins acquire context-specific functions, despite widespread embryonic expression of Pbx1/2, remains elusive. In mouse limb buds, mesenchymal-specific loss of PBX1/2 or of the transcriptional regulator HAND2 results in similar phenotypes, suggesting that PBX1/2- and HAND2-dependent programs converge to control limb development. To investigate this scenario, we combined tissue-specific and temporally-controlled mutagenesis with multi-omics approaches using the murine hindlimb bud as a model. We reconstructed a GRN collaboratively directed by PBX1/2 and HAND2, demonstrating that Pbx1-Hand2 genetically interact in vivo during hindlimb patterning, with PBX1 concomitantly acting as an upstream regulator of Hand2. At organismal-level resolution the GRN is active within restricted subsets of posterior-proximal hindlimb mesenchymal cells, wherein Pbx1/2 and Hand2 are co-expressed with their target genes. Genome-wide profiling of PBX1 binding across multiple tissues further revealed that HAND2 selects a subset of PBX-bound regions to impart limb patterning functionality. This research elucidates mechanisms underlying limb bud-specific functions by PBX1/2, while informing general principles by which promiscuous transcription factors cooperate with select cofactors to instruct distinct developmental programs.

developmental biology↗

bioRxiv2019

HOX paralogs selectively convert binding of ubiquitous transcription factors into tissue-specific patterns of enhancer activation

Gene expression programs determine cell fate in embryonic development and their dysregulation results in disease. Transcription factors (TFs) control gene expression by binding to enhancers, but how TFs select and activate their target enhancers is still unclear. HOX TFs share conserved homeodomains with highly similar sequence recognition properties, yet they impart the identity of different animal body parts. To understand how HOX TFs control their specific transcriptional programs in vivo, we compared HOXA2 and HOXA3 binding profiles in the mouse embryo. HOXA2 and HOXA3 directly cooperate with TALE TFs and selectively target different subsets of a broad TALE chromatin platform. Binding of HOX and tissue-specific TFs convert low affinity TALE binding into high confidence, tissue-specific binding events, which bear the mark of active enhancers. We propose that HOX paralogs, alone and in combination with tissue-specific TFs, generate tissue-specific transcriptional outputs by modulating the activity of TALE TFs at selected enhancers.

developmental biology↗

bioRxiv2019

Simultaneous analysis of open chromatin, promoter interactions and gene expression in stimulated T cells implicates causal genes for rheumatoid arthritis

Genome-wide association studies have identified genetic variation contributing to complex disease risk. However, assigning causal genes and mechanisms has been more challenging because disease-associated variants are often found in distal regulatory regions with cell-type specific behaviours. Here, we collect ATAC-seq, Hi-C, Capture Hi-C and nuclear RNA-seq data in stimulated CD4+ T-cells over 24 hours, to identify functional enhancers regulating gene expression. We characterise changes in DNA interaction and activity dynamics that correlate with changes gene expression, and find that the strongest correlations are observed within 200 kb of promoters. Using rheumatoid arthritis as an example of T-cell mediated disease, we demonstrate interactions of expression quantitative trait loci with target genes, and confirm assigned genes or show complex interactions for 20% of disease associated loci, including FOXO1, which we confirm using CRISPR/Cas9.

genomics↗

bioRxiv2019

Uncovering tissue-specific binding features from differential deep learning

MotivationTranscription factors (TFs) can bind DNA in a cooperative manner, enabling a mutual increase in occupancy. Through this type of interaction, alternative binding sites can be preferentially bound in different tissues to regulate tissue-specific expression programmes. Recently, deep learning models have become state-of-the-art in various pattern analysis tasks, including applications in the field of genomics. We therefore investigate the application of convolutional neural network (CNN) models to the discovery of sequence features determining cooperative and differential TF binding across tissues. ResultsWe analyse ChIP-seq data from MEIS, TFs which are broadly expressed across mouse branchial arches, and HOXA2, which is expressed in the second and more posterior branchial arches. By developing models predictive of MEIS differential binding in all three tissues we are able to accurately predict HOXA2 co-binding sites. We evaluate transfer-like and multitask approaches to regularising the high-dimensional classification task with a larger regression dataset, allowing for creation of deeper and more accurate models. We test the performance of perturbation and gradient-based attribution methods in identifying the HOXA2 sites from differential MEIS data. Our results show that deep regularised models significantly outperform shallow CNNs as well as k-mer methods in the discovery of tissue-specific sites bound in vivo. AvailabilityFor implementation and models please visit https://doi.org/10.5281/zenodo.2635463.

bioinformatics↗