Biology subjects

Syed, H.

Publications and source records attributed to Syed, H..

11 recordsLinked to original sources

bioRxiv2026

TransXplorer: An automated translational discovery platform for RNA-seq data

RNA-seq experiments routinely identify thousands of differentially expressed genes, but translating these into biological insights and therapeutic hypotheses often requires integrating multiple tools. Existing web platforms such as iDEP, NetworkAnalyst, and GEPIA2 address individual steps, differential expression, network visualization, or TCGA queries, but lack a unified environment spanning raw data processing to clinical and pharmacological interpretation. TransXplorer (https://transxplorer.org) is a freely available web platform that addresses this limitation by integrating the complete RNA-seq analytical workflow. It supports processing from raw FASTQ files using HISAT2 or Salmon, as well as direct GEO dataset import with automated metadata handling. Differential expression analysis is implemented via DESeq2, edgeR, and limma-voom, followed by functional enrichment across more than 1,800 species using Bioconductor resources. Batch effects are automatically detected and corrected using a composite of PVCA, kBET, and Silhouette metrics without requiring predefined batch annotations. Downstream analyses include co-expression network construction (WGCNA), protein-protein interaction mapping (STRING), cell-type deconvolution, and transcription factor inference using integrated DoRothEA and TFLink resources. The platform further links gene signatures to drug candidates through DGIdb and OpenTargets and enables survival and tumour-normal comparisons across TCGA cohorts. Application to cardiac endothelial differentiation (GSE151427) and kidney renal papillary cell carcinoma (TCGA-KIRP) datasets demonstrates accurate batch correction, biologically consistent pathway enrichment, recovery of expected cell-type proportions, and identification of clinically relevant genes and drug candidates. TransXplorer is freely available without a login.

bioinformatics↗

bioRxiv2025

Monoaminergic neurons share transcriptional identity across Bilaterian animals

The evolutionary conservation of cell types over deep time has long been theorised but remains difficult to demonstrate. Monoaminergic neurons, which produce molecules such as serotonin and dopamine, are central to animal behaviour and cognition, yet their evolutionary origins remain unresolved. Here, we analysed single-cell transcriptomes from 16 metazoan species spanning eight phyla. Using a novel integration method, Orthogroup Recoding, together with experimental validation, we show that monoaminergic neurons form consistent transcriptional clusters across Bilateria. These clusters share a regulatory signature involving conserved transcription factors, including homologues of Fev, Lmx1b, Fer2, Insm, and Isl, suggesting a common regulatory program. This signature extends beyond the brain including other monoaminergic cells, such as the gut enterochromaffin cells and larval sensory neurons. By contrast, non-bilaterian lineages lack the coordinated expression of the transcription factors and the biosynthetic machinery that regulate monoamine production. Our observations support the existence of a shared set of TFs that define monoaminergic identity across in bilaterians.

bioinformatics↗

bioRxiv2025

Functional Genomic Screens Reveal RBBP4 as a Key Regulator of Cell Cycle Progression in TMZ-Resistant Glioblastoma

Temozolomide (TMZ) remains the standard of care for glioblastoma; however, its efficacy is frequently influenced by epigenetic mechanisms, notably the methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter. While MGMT promoter hypermethylation is associated with enhanced responsiveness to TMZ, additional epigenetic determinants of TMZ resistance remain largely undefined. In this study, we established TMZ-resistant glioblastoma cell lines that consistently maintained their resistant phenotype both in vitro and in vivo. Transcriptomic analyses revealed a marked upregulation of MGMT expression in these models. To systematically investigate the epigenetic regulators governing TMZ resistance and cell survival, we conducted CRISPR/Cas9-based functional genomic screens using our focused Epigenetic Knock-Out Library (EPIKOL), which targets 800 chromatin regulators alongside selected positive and negative controls. These unbiased screens validated MGMT as a primary mediator of TMZ resistance, confirming the robustness of our approach. Moreover, dropout screens across multiple resistant cell line models identified Retinoblastoma Binding Protein 4 (RBBP4) as a critical vulnerability. Notably, RBBP4 knockout significantly impaired cell proliferation without affecting MGMT expression, suggesting a distinct mechanism supporting the survival of TMZ-resistant glioblastoma cells. Subsequent transcriptomic profiling following RBBP4 loss demonstrated significant downregulation of cell cycle pathways, particularly the G2/M checkpoint. Live-cell imaging and immunofluorescence analyses further revealed increased cell size and multinucleation in RBBP4-deficient cells, indicative of disrupted mitotic progression. Collectively, our results identify RBBP4 as a key regulator of cell cycle progression and survival in TMZ-resistant glioblastoma and highlight its potential as a novel epigenetic target for therapeutic intervention in recurrent disease.

cancer biology↗

bioRxiv2024

Epidrug Screening Identifies Type I PRMT Inhibitors as Modulators of Lysosomal Exocytosis and Drug Sensitivity in Cancers

Epigenetic changes drive differential gene expression, contributing to oncogenic transformation and drug resistance. Lysosomes are crucial in cell signaling and the sequestration of toxins and chemotherapeutic agents. This sequestration followed by expulsion through lysosomal exocytosis is a factor in drug resistance. The epigenetic regulation of lysosomal exocytosis remains poorly understood. Our research focuses on this regulation, hypothesizing that epigenetic modifier drugs (epidrugs) capable of inhibiting lysosomal exocytosis and could serve as potential therapeutics. Additionally, we investigate their potential synergy with drugs known to be sequestered in lysosomes. To examine this concept, we screened approximately 150 epigenetic drugs targeting various reader, writer, or eraser proteins. These drugs were assessed for their combined cytotoxic effects with cisplatin, their impact on lysosomal exocytosis, and on lysosomal biogenesis. Our findings reveal that among the epidrugs showing synergy with cisplatin and further reducing cell viability in combination, two type I PRMT inhibitors, MS023 and GSK3368715, inhibited lysosomal exocytosis. Notably, neither of these drugs altered the expression of the CLEAR lysosomal biogenesis network of genes, suggesting the involvement of novel regulators in lysosomal functions. To explore the specific components of the trafficking machinery affected by PRMT inhibitors, we conducted an RNA-seq analysis, uncovering several differentially expressed genes (DEGs). In addition to previously described functions such as methylation activity, or DNA repair; these DEGs included those involved in vesicular trafficking, lysosomal enzyme activity and lysosome dynamics, offering potential insights into the mechanism of reduced exocytosis and identifying a novel mode for its regulation. Additionally, both inhibitors exhibited synergy with other drugs known to be sequestered in lysosomes, such as carboplatin, oxaliplatin, sunitinib, and doxorubicin, indicating that inhibition of lysosomal exocytosis may be a common phenomenon for such drugs. These findings underscore the potential of Type I PRMT inhibitors as therapeutic agents in cancer treatment. Consistently, analysis on the publicly available patient data revealed that lower levels of type I PRMTs (PRMT1 and 6) were associated with better patient response to these drugs, further suggesting their potential as drug candidates for combination therapy to enhance chemotherapy efficacy and improve cancer patient survival rates.

cancer biology↗

bioRxiv2024

Chromatin-focused genetic and chemical screens identify BRPF1 as a targetable vulnerability in Taxol-resistant triple-negative breast cancer

Triple-negative breast cancer (TNBC) stands out as a particularly aggressive and frequently recurring form of breast cancer. Due to the absence of hormone receptors, the available treatment avenues are constrained, making chemotherapy the primary approach. Unfortunately, the development of resistance to chemotherapy poses a significant challenge, further restricting the already limited therapeutic alternatives for recurrent cases. Understanding the molecular basis of chemotherapy resistance in TNBC is pivotal for improving treatment outcomes. Here, we generated two different Taxol-resistant TNBC cell lines with a dose-escalation method to mimic chemotherapy resistance in vitro. These cells exhibited hallmark features of resistance, including reduced cell growth, altered morphology, and evasion of apoptosis. Transcriptome analysis uncovered elevated ABCB1 expression and multidrug-resistant phenotype in the resistant cells. To comprehensively investigate the key epigenetic regulators of Taxol resistance, we conducted chromatin-focused genetic and chemical screens and pinpointed Bromodomain and PHD Finger Containing 1 (BRPF1) as a novel regulator of Taxol resistance in TNBC cells. Knockout of BRPF1, the reader protein in the MOZ/MORF histone acetyl-transferase complex, but not the other complex members, sensitized resistant cells to Taxol. Additionally, BRPF1 inhibitors, PFI-4 and OF-1, in combination with Taxol significantly reduced cell viability. Transcriptome analysis upon BRPF1 loss or inhibition revealed a negative impact on ribosome biogenesis-related gene sets, resulting in a global decrease in protein translation in Taxol-resistant cells. Our ChIP-qPCR analysis demonstrated that active BRPF1 directly interacts with the ABCB1 promoter, enhancing its expression towards inducing a multidrug-resistant phenotype. Conversely, knockout or inhibition of BRPF1 leads to decreased ABCB1 expression. This dual mechanism critically sensitizes Taxol-resistant TNBC cells to chemotherapy. Our findings uncover a comprehensive molecular framework, highlighting the pivotal role of epigenetic reader protein BRPF1 in Taxol resistance and providing potential avenues for therapeutic intervention in TNBC.

cancer biology↗

bioRxiv2023

Exploiting Epigenetic Targets to Overcome Taxane Resistance in Prostate Cancer

The development of taxane resistance remains a major challenge for castration resistant prostate cancer (CR-PCa), despite the effectiveness of taxanes in prolonging patient survival. To uncover novel targets, we performed an epigenetic drug screen on taxane (docetaxel and cabazitaxel) resistant CR-PCa cells. We identified BRPF reader proteins, along with several epigenetic groups (CBP/p300, Menin-MLL, PRMT5 and SIRT1) that act as targets effectively reversing the resistance mediated by ABCB1. Targeting BRPFs specifically resulted in the resensitization of resistant cells, while no such effect was observed on the sensitive compartment. These cells were successfully arrested at the G2/M phase of cell cycle and underwent apoptosis upon BRPF inhibition, confirming the restoration of taxane susceptibility. Pharmacological inhibition of BRPFs reduced ABCB1 activity, indicating that BRPFs may be involved in an efflux-related mechanism. Indeed, ChIP-qPCR analysis confirmed binding of BRPF1 to the ABCB1 promoter suggesting direct regulation of the ABCB1 gene at the transcriptional level. RNA-seq analysis revealed that BRPF1 knockdown affects the genes enriched in mTORC1 and UPR signaling pathways, revealing potential mechanisms underlying its functional impact, which is further supported by the enhancement of taxane response through the combined inhibition of ABCB1 and mTOR pathways, providing evidence for the involvement of multiple BRPF1-regulated pathways. Beyond clinical attributes (Gleason score, tumor stage, therapy outcome, recurrence), metastatic PCa databases further supported the significance of BRPF1 in taxane resistance, as evidenced by its upregulation in taxane-exposed PCa patients.

cancer biology↗

bioRxiv2023

Mouse mammary tumor virus is implicated in severity of colitis and dysbiosis in the IL-10-/- mouse model of inflammatory bowel disease

BackgroundFollowing viral infection, genetically manipulated mice lacking immunoregulatory function may develop colitis and dysbiosis in a strain specific fashion that serves as a model for inflammatory bowel disease (IBD). We found that one such model of spontaneous colitis, the interleukin (IL)-10 knockout (IL-I0-/-) model derived from the SvEv mouse, had evidence of increased mouse mammary tumor virus (MMTV) viral RNA expression compared to the SvEv wildtype. MMTV is endemic in several mouse strains as an endogenously encoded betaretrovirus that is passaged as an exogenous agent in breast milk. As MMTV requires a viral superantigen to replicate in the gut associated lymphoid tissue prior to the development of systemic infection, we evaluated whether MMTV may contribute to the development of colitis in the IL-10-/- model. ResultsViral preparations extracted from IL-10-/- weanling stomachs revealed augmented MMTV load compared to the SvEv wildtype. Illumina sequencing of the viral genome revealed that the two largest contigs shared 96.4% - 97.3% identity with the mtv-1 endogenous loci and the MMTV(HeJ) exogenous virus from the C3H mouse. The MMTV sag gene cloned from IL-10-/- spleen encoded the MTV-9 superantigen that preferentially activates T cell receptor V{beta}-12 subsets, which were expanded in the IL-10-/- versus the SvEv colon. Evidence of MMTV cellular immune responses to MMTV Gag peptides was observed in the IL-10-/- splenocytes with amplified interferon-{gamma} production versus the SvEv wildtype. To address the hypothesis that MMTV may contribute to colitis, we used HIV reverse transcriptase inhibitors, tenofovir and emtricitabine, and the HIV protease inhibitor, lopinavir boosted with ritonavir, for 12 weeks treatment versus placebo. The combination anti-retroviral therapy with known activity against MMTV was associated with reduced colonic MMTV RNA and improved histological score in IL10-/- mice, as well as diminished secretion of pro-inflammatory cytokines and modulation of the microbiome associated with colitis. ConclusionsThis study suggests that immunogenetically manipulated mice with deletion of IL-10 may have reduced capacity to contain MMTV infection in a mouse-strain specific manner, and the antiviral inflammatory responses may contribute to the complexity of IBD with the development of colitis and dysbiosis.

microbiology↗

bioRxiv2023

The Use of Class Imbalanced Learning Methods on ULSAM Data to Predict the Case-Control Status in Genome-Wide Association Studies

Machine learning (ML) methods for uncovering single nucleotide polymorphisms (SNPs) in genome-wide association study (GWAS) data that can be used to predict disease outcomes are becoming increasingly used in genetic research. Two issues with the use of ML models are finding the correct method for dealing with imbalanced data and data training. This article compares three ML models to identify SNPs that predict type 2 diabetes (T2D) status using the Support vector machine SMOTE (SVM SMOTE), The Adaptive Synthetic Sampling Approach (ADASYN), Random under sampling (RUS) on GWAS data from elderly male participants (165 cases and 951 controls) from the Uppsala Longitudinal Study of Adult Men (ULSAM). It was also applied to SNPs selected by the SMOTE, SVM SMOTE, ADASYN, and RUS clumping method. The analysis was performed using three different ML models: (i) support vector machine (SVM), (ii) multilayer perceptron (MLP) and (iii) random forests (RF). The accuracy of the case-control classification was compared between these three methods. The best classification algorithm was a combination of MLP and SMOTE (97% accuracy). Both RF and SVM achieved good accuracy results of over 90%. Overall, methods used against unbalanced data, all three ML algorithms were found to improve prediction accuracy.

bioinformatics↗

bioRxiv2022

Epigenetic-focused CRISPR/Cas9 screen identifies ASH2L as a regulator of glioblastoma cell survival

Glioblastoma is the most common and aggressive primary brain tumor with poor prognosis, highlighting an urgent need for novel treatment strategies. In this study, we investigated epigenetic regulators of glioblastoma cell survival through CRISPR/Cas9 based genetic ablation screens using a customized sgRNA library EpiDoKOL, which targets critical functional domains of chromatin modifiers. Screens conducted in multiple cell lines revealed ASH2L, a histone lysine methyltransferase complex subunit, as a major regulator of glioblastoma cell viability. ASH2L depletion led to cell cycle arrest and apoptosis. RNA sequencing and greenCUT&RUN together identified a set of cell cycle regulatory genes, such as TRA2B, BARD1, KIF20B, ARID4A and SMARCC1 that were downregulated upon ASH2L depletion. Mass spectrometry analysis revealed the interaction partners of ASH2L in glioblastoma cell lines as SET1/MLL family members including SETD1A, SETD1B, MLL1 and MLL2. We further showed that glioblastoma cells had a differential dependency on expression of SET1/MLL family members for survival. The growth of ASH2L-depleted glioblastoma cells was markedly slower than controls in orthotopic in vivo models. TCGA analysis showed high ASH2L expression in glioblastoma compared to low grade gliomas and immunohistochemical analysis revealed significant ASH2L expression in glioblastoma tissues, attesting to its clinical relevance. Therefore, high throughput, robust and affordable screens with focused libraries, such as EpiDoKOL, holds great promise to enable rapid discovery of novel epigenetic regulators of cancer cell survival, such as ASH2L. Together, we suggest that targeting ASH2L could serve as a new therapeutic opportunity for glioblastoma.

cancer biology↗

bioRxiv2021

rareSurvival: rare variant association analysis for time-to-event outcomes.

SummaryRare variants have been proposed as contributing to the "missing heritability" of complex human traits. There has been much recent development of methodology to investigate association of complex traits with multiple rare variants within pre-defined "units" from sequence and array-based studies of the exome or genome. However, software for modelling time to event outcomes for rare variant associations has been under developed in comparison with binary and quantitative traits. We introduce a new command line application, rareSurvival, used for the analysis of rare variants with time to event outcomes. The program is compatible with high performance computing (HPC) clusters for batch processing. rareSurvival implements statistical methodology, which are a combination of widely used survival and gene-based analysis techniques such as the Cox proportional hazards model and the burden test. We introduce a novel piece of software that will be at the forefront of efforts to discover rare variants associated with a variety of complex diseases with survival endpoints. Availability & ImplementationrareSurvival is implemented in C#, available on Linux, Windows and Mac OS X operating systems. It is freely available (GNU General Public License, version 3) to download from https://www.liverpool.ac.uk/translational-medicine/research/statistical-genetics/software/. Download Mono for Linux or Mac OS X to run software. Contacthamzah.syed@liverpool.ac.uk Supplementary informationLinks to additional figures and tables are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

MOPower: an R-shiny application for the simulation and power calculation of multi-omics studies.

BackgroundMulti-omics studies are increasingly used to help understand the underlying mechanisms of clinical phenotypes, integrating information from the genome, transcriptome, epigenome, metabolome, proteome and microbiome. This integration of data is of particular use in rare disease studies where the sample sizes are often relatively small. Methods development for multi-omics studies is in its early stages due to the complexity of the different individual data types. There is a need for software to perform data simulation and power calculation for multi-omics studies to test these different methodologies and help calculate sample size before the initiation of a study. This software, in turn, will optimise the success of a study. ResultsThe interactive R shiny application MOPower described below simulates data based on three different omics using statistical distributions. It calculates the power to detect an association with the phenotype through analysis of n number of replicates using a variety of the latest multi-omics analysis models and packages. The simulation study confirms the efficiency of the software when handling thousands of simulations over ten different sample sizes. The average time elapsed for a power calculation run between integration models was approximately 500 seconds. Additionally, for the given study design model, power varied with the increase in the number of features affecting each method differently. For example, using MOFA had an increase in power to detect an association when the study sample size equally matched the number of features. ConclusionsMOPower addresses the need for flexible and user-friendly software that undertakes power calculations for multi-omics studies. MOPower offers users a wide variety of integration methods to test and full customisation of omics features to cover a range of study designs.

bioinformatics↗