Biology subjects

Mughal, F.

Publications and source records attributed to Mughal, F..

4 recordsLinked to original sources

bioRxiv2024

Evolution of intrinsic disorder in the structural domains of viral and cellular proteomes

Intrinsically disordered regions are flexible regions that complement the typical structured regions of proteins. Little is known however about their evolution. Here we leverage a comparative and evolutionary genomics approach to analyze intrinsic disorder in the structural domains of thousands of proteomes. Our analysis revealed that viral and cellular proteomes employ similar strategies to increase disorder but achieve different goals. Viral proteomes evolve disorder for economy of genomic material and multifunctionality. On the other hand, cellular proteomes evolve disorder to advance functionality with increasing genomic complexity. Remarkably, phylogenomic analysis of intrinsic disorder showed that ancient domains were ordered and that disorder evolved as a benefit acquired later in evolution. Evolutionary chronologies of domains indexed with disorder levels and distributions across Archaea, Bacteria, Eukarya and viruses revealed six evolutionary phases, the oldest two harboring only ordered and moderate disorder domains and delimiting two universal common ancestors of life. A biphasic spectrum of disorder versus proteome makeup captured the dichotomy in the evolutionary trajectories of viral and cellular ancestors, one following reductive evolution driven by viral spread of molecular wealth and the other following expansive evolutionary trends to advance functionality through massive domain-forming co-option of disordered loop regions. TeaserComparative and evolutionary genomic analyses of the intrinsic disorder of structural domains in thousands of proteomes reveal proteomes increase disorder as a benefit acquired later in evolution to support viral economy and multifunctionality or advanced cellular functionality.

evolutionary biology↗

bioRxiv2024

SARS-CoV-2 S, M and E Structural Proteins Down-modulate HIV-1 LTR Activity and Modulate Endoplasmic Reticulum Stress Responses

We have previously shown that the Hepatitis C Virus (HCV) E1E2 envelope glycoprotein can down-modulate HIV-1 long-terminal repeat (LTR) activity through disruption to NF-{kappa}B activation. This response is associated with up-regulation of the endoplasmic reticulum (ER) stress response pathway. Here we demonstrate that the SARS-CoV-2 S, M and E but not the N structural protein can perform similar down-modulation of HIV-1 LTR activation and in a dose-dependent manner in both HEK293 and lung BEAS-2B cell-lines and interpreted as a result of NF-{kappa}B down-modulation. The effect is highest with the SARS-CoV-2 Wuhan S strain and decreases over-time for the subsequent emerging variants of concern (VOC) with omicron providing the weakest effect. We developed pseudo-typed viral particle (PVP) molecular viral tools that allowed for the generation of cell-lines constitutively expressing separately the four SARS-CoV-2 structural proteins and utilising the VSV-g envelope protein to deliver the integrated gene construct. Differential gene expression analysis (DGEA) was performed on cells expressing S, E, M or N to determine cell activation status. It it was determined that gene expression differences were found in a number of interferon-stimulated genes (ISGs), including IF16, IFIT1, IFIT2 and ISG15 as well as for a number of heat shock protein (HSP) genes, including HSPH1, HSPA6 and HSPBP1 with all four SARS-CoV-2 structural proteins. There were also differences observed with expression patterns of transcription factors with both SP1 and MAVS upregulated in the presence of S, M and E but not the N protein. Collectively the results indicate that gene expression patterns associating with ER stress pathways can be identified with SARS-CoV-2 envelope glycoprotein expression. The results suggest the SARS-CoV-2 can modulate activation of an array of cell pathways resulting in disruption to NF-{kappa}B signalling hence providing alterations to multiple physiological responses of SARS-CoV-2 infected cells.

immunology↗

bioRxiv2020

An untargeted metabolomics strategy to measure differences in metabolite uptake and excretion by mammalian cell lines

IntroductionIt is widely but erroneously believed that drugs get into cells by passing through the phospholipid bilayer portion of the plasma and other membranes. Much evidence shows, however, that this is not the case, and that drugs cross biomembranes by hitchhiking on transporters for other natural molecules to which these drugs are structurally similar. Untargeted metabolomics can provide a method for determining the differential uptake of such metabolites. ObjectivesBlood serum contains many thousands of molecules and provides a convenient source of biologically relevant metabolites. Our objective was to measure them. MethodsWe develop an untargeted LC-MS/MS method to detect a broad range of compounds present in human serum. We apply this to the analysis of the time course of the uptake and secretion of metabolites in serum by several human cell lines, by analysing changes in the serum that represents the extracellular phase (the exometabolome or metabolic footprint). ResultsOur method measures some 4,000-5,000 metabolic features in both ES+ and ES- modes. We show that the metabolic footprints of different cell lines differ greatly from each other. ConclusionOur new, 15-minute untargeted metabolome method allows for the robust and convenient measurement of differences in the uptake of serum compounds by cell lines following incubation in serum, and its relation to differences in transporter expression.

biochemistry↗

bioRxiv2019

The role and robustness of the Gini coefficient as an unbiased tool for the selection of Gini genes for normalising expression profiling data

We recently introduced the Gini coefficient (GC) for assessing the expression variation of a particular gene in a dataset, as a means of selecting improved reference genes over the cohort ( housekeeping genes) typically used for normalisation in expression profiling studies. Those genes (transcripts) that we determined to be useable as reference genes differed greatly from previous suggestions based on hypothesis-driven approaches. A limitation of this initial study is that a single (albeit large) dataset was employed for both tissues and cell lines.\n\nWe here extend this analysis to encompass seven other large datasets. Although their absolute values differ a little, the Gini values and median expression levels of the various genes are well correlated with each other between the various cell line datasets, implying that our original choice of the more ubiquitously expressed low-Gini-coefficient genes was indeed sound. In tissues, the Gini values and median expression levels of genes showed a greater variation, with the GC of genes changing with the number and types of tissues in the data sets. In all data sets, regardless of whether this was derived from tissues or cell lines, we also show that the GC is a robust measure of gene expression stability. Using the GC as a measure of expression stability we illustrate its utility to find tissue- and cell line-optimised housekeeping genes without any prior bias, that again include only a small number of previously reported housekeeping genes. We also independently confirmed this experimentally using RT-qPCR with 40 candidate GC genes in a panel of 10 cell lines. These were termed the Gini Genes.\n\nIn many cases, the variation in the expression levels of classical reference genes is really quite huge (e.g. 44 fold for GAPDH in one data set), suggesting that the cure (of using them as normalising genes) may in some cases be worse than the disease (of not doing so). We recommend the present data-driven approach for the selection of reference genes by using the easy-to-calculate and robust GC.

molecular biology↗