Biology subjects

Ghanate, A.

Publications and source records attributed to Ghanate, A..

4 recordsLinked to original sources

bioRxiv2023

Discovery of multi-state gene cluster switches determining the adaptive mitochondrial and metabolic landscape of breast cancer

Adaptive metabolic switches are proposed to underlie conversions between cellular states during normal development as well as in cancer evolution, where they represent important therapeutic targets. However, the full spectrum, characteristics and regulation of existing metabolic switches are unknown. We propose that metabolic switches can be recognised by locating large alternating gene expression patterns and associate them with specific metabolic states. We developed a method to identify interspersed genesets by massive correlated biclustering (MCbiclust) and to predict their metabolic wiring. Testing the method on major breast cancer transcriptome datasets we discovered a series of gene sets with switch-like behaviour, predicting mitochondrial content, activity and central carbon fluxes in tumours associated with different switch positions. The predictions were experimentally validated by bioenergetic profiling and metabolic flux analysis of 13C-labelled substrates and were ultimately extended by geneset analysis to link metabolic alterations to cellular states, thus predicting tumour pathology, prognosis and chemosensitivity. The method is applicable to any large and heterogeneous transcriptome dataset to discover metabolic and associated pathophysiological states. Statement of significanceWe present a novel method to identify the transcriptomic signatures of metabolic switches underlying divergent routes of cellular transformation. We illustrate the power of the method by stratifying breast cancer into metabolic subtypes, predicting their biology, architecture and clinical outcome.

systems biology↗

bioRxiv2022

ASCL1 interacts with the mSWI/SNF at distal regulatory elements to regulate neural differentiation

AO_SCPLOWBSTRACTC_SCPLOWPioneer transcription factors are thought to play pivotal roles in developmental processes by binding nucleosomal DNA to activate gene expression. The role of neurogenic pioneer factor ASCL1 in shaping chromatin landscape in human neurogenesis remains unclear. Here we show that ASCL1 acts as a pioneer transcription factor in a transient population of progenitors. Using an in vitro ASCL1 knockout model we show it drives progenitor differentiation by cis-regulation both as a classical pioneer factor and as a non-pioneer remodeler, where ASCL1 binds permissive chromatin to induce chromatin conformation changes. We find ASCL1 directly interacts with mammalian BAF SWI/SNF chromatin remodeling complexes, essential for neurogenesis and involved in multiple neurodevelopmental disorders. ASCL1 acts as a non-pioneer chromatin remodeler to regulate gene expression at a subset of loci, requiring mBAF SWI/SNFs ATPase activity for cis-regulation of gene expression. Our findings demonstrate that ASCL1 is a key chromatin remodeler in human neurogenesis, uncovering an alternative mechanism of remodeling function dependent on partner ATPase activity.

developmental biology↗

bioRxiv2020

MQ: Tool for HR-AM Based Quantitative Analysis for Direct MS Workflow

There is an increased interest in using high-resolution mass spectrometry (HRMS) in the full scan mode (FS) for simultaneous qualitative and quantitative analysis with accurate mass based extracted ion chromatograms (HRMS-AM) for metabolomic profiling workflows. Herein, the capabilities and features of MQ, a common platform developed to support quantitative analysis from HRMS data are described. Module for qualitative annotation utilizes peak features that include mass accuracy, peak width, and relative abundance of isotopic distribution that offer reliable metabolic confirmation. Additionally, MQ provides multivariate clustering with Principal Component Analysis and a module for high-throughput database query based on user configurable annotated metabolite lists. MQ is capable of handling system-agnostic data formats from both direct HRMS and LC-HRMS analysis. Data processed using MQ for LC-HRMS-AM quantitation of amino acids benchmarked using a commercial tool is presented that validates the quantitative capabilities of the algorithm developed. Datasets representative of three different HRMS-AM based analysis scenarios have been showcased to demonstrate the utility of MQ. Availability and implementationFreely available on web at, http://www.ldims.com/services/software. MQ is implemented in Java and supported on Linux and Microsoft Windows system having Java runtime environment (JRE) pre-installed.

bioinformatics↗

bioRxiv2019

Perturbation of the gut microbiota by antibiotics results in accelerated breast tumour growth and metabolic dysregulation

BackgroundBreast cancer is the second most prevalent cancer worldwide with around 1.7 million new cases diagnosed every year. Whilst prognosis is generally favourable in early stages, this worsens significantly in advanced disease. Therefore, it is pertinent to focus on mitigating factors that may slow growth or progression. Recently, the gut microbiome has been implicated in a wide-range of roles in tumour biology. Through modulation of immunity, the gut microbiota can improve the efficacy of several immunotherapies. However, despite the prevalence of breast cancer, there is still a lack of microbiota studies in this field, including exploring the influence of external microbiome-modulating factors such as antibiotics. We describe herein how disruption of the gut microbiota via antibiotics may be detrimental to patient outcomes through acceleration of tumour growth. ResultsSupplementing animals with a cocktail of antibiotics leads to gut microbiota alterations and is accompanied by significant acceleration of tumour growth. Surprisingly, and distinct from previous microbiome-tumour studies, the mechanism driving these effects do not appear to be due to gross immunological changes. Analysis of intratumoural immune cell populations and cytokine production are not affected by antibiotic administration. Through global tumour transcriptomics, we have uncovered dysregulated gene expression networks relating to protein and lipid metabolism that are correlated with accelerated tumour growth. Fecal metabolomics revealed a reduction of the microbial-derived short-chain fatty acid butyrate that may contribute to accelerated tumour growth. Finally, through use of a routinely administered antibiotic in breast cancer patients, Cephalexin, we have shown that tumour growth is also significantly affected. Metataxanomic sequencing and analysis highlighted significant antibiotic-associated reductions in the butyrate producing genera Odoribacter and Anaeotruncus, and increased abundance of Bacteroides. ConclusionsOur data indicate that perturbation of the microbiota by antibiotics may have negative impacts on breast cancer patient outcomes. This is of importance as antibiotics are regularly prescribed to breast cancer patients undergoing mastectomy or breast reconstruction. We have also shown that the metabolic impact of disruption to the microbiome should be considered alongside the potent immunological effects. We believe our work lays the foundation for improving the use of antibiotics in patients, and with further investigation could potentially inform clinical practice.

cancer biology↗