5,404 research outputs found

    Gene markers of dietary macronutrient composition and growth in the skeletal muscle of gilthead sea bream (Sparus aurata)

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    To increase our current knowledge on the nutritional regulation of growth and gene expression pattern in fish skeletal muscle, the effect of dietary macronutrient composition was assessed on digestibility, nutrient retention, growth performance, and the mRNA levels of key genes involved in functionality, growth and development of the skeletal muscle in gilthead sea bream (Sparus aurata). Long-term starvation decreased the expression of myogenic regulatory factors such as Myod2, Myf5, myogenin (Myog) and Myf6 in the skeletal muscle of S. aurata. The supply of high or medium protein, low carbohydrate diets enhanced growth parameters, feed efficiency ratio, feed conversion ratio and significantly upregulated myod2. However, the supply of low protein, high carbohydrate diets restricted growth and stimulated the mRNA levels of myostatin, while downregulated follistatin (fst), igf1, mtor and rps6. Microarray analysis revealed igfals, tnni2, and gadd45a as gene markers upregulated by diets enriched with protein, lipids and carbohydrates, respectively. The results of the present study show that in addition to myod2, fst, igf1, mtor and rps6, the expression levels of igfals, tnni2 and remarkably gadd45a in the skeletal muscle can be used as markers to evaluate the effect of dietary macronutrient changes on fish growth and muscle development in S. aurata

    Targeting Fusion Proteins of HIV-1 and SARS-CoV-2

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    Viruses are disease-causing pathogenic agents that require host cells to replicate. Fusion of host and viral membranes is critical for the lifecycle of enveloped viruses. Studying viral fusion proteins can allow us to better understand how they shape immune responses and inform the design of therapeutics such as drugs, monoclonal antibodies, and vaccines. This thesis discusses two approaches to targeting two fusion proteins: Env from HIV-1 and S from SARS-CoV-2. The first chapter of this thesis is an introduction to viruses with a specific focus on HIV-1 CD4 mimetic drugs and antibodies against SARS-CoV-2. It discusses the architecture of these viruses and fusion proteins and how small molecules, peptides, and antibodies can target these proteins successfully to treat and prevent disease. In addition, a brief overview is included of the techniques involved in structural biology and how it has informed the study of viruses. For the interested reader, chapter 2 contains a review article that serves as a more in-depth introduction for both viruses as well as how the use of structural biology has informed the study of viral surface proteins and neutralizing antibody responses to them. The subsequent chapters provide a body of work divided into two parts. The first part in chapter 3 involves a study on conformational changes induced in the HIV-1 Env protein by CD4-mimemtic drugs using single particle cryo-EM. The second part encompassing chapters 4 and 5 includes two studies on antibodies isolated from convalescent COVID-19 donors. The former involves classification of antibody responses to the SARS-CoV-2 S receptor-binding domain (RBD). The latter discusses an anti-RBD antibody class that binds to a conserved epitope on the RBD and shows cross-binding and cross-neutralization to other coronaviruses in the sarbecovirus subgenus.</p

    Application of advanced fluorescence microscopy and spectroscopy in live-cell imaging

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    Since its inception, fluorescence microscopy has been a key source of discoveries in cell biology. Advancements in fluorophores, labeling techniques and instrumentation have made fluorescence microscopy a versatile quantitative tool for studying dynamic processes and interactions both in vitro and in live-cells. In this thesis, I apply quantitative fluorescence microscopy techniques in live-cell environments to investigate several biological processes. To study Gag processing in HIV-1 particles, fluorescence lifetime imaging microscopy and single particle tracking are combined to follow nascent HIV-1 virus particles during assembly and release on the plasma membrane of living cells. Proteolytic release of eCFP embedded in the Gag lattice of immature HIV-1 virus particles results in a characteristic increase in its fluorescence lifetime. Gag processing and rearrangement can be detected in individual virus particles using this approach. In another project, a robust method for quantifying Förster resonance energy transfer in live-cells is developed to allow direct comparison of live-cell FRET experiments between laboratories. Finally, I apply image fluctuation spectroscopy to study protein behavior in a variety of cellular environments. Image cross-correlation spectroscopy is used to study the oligomerization of CXCR4, a G-protein coupled receptor on the plasma membrane. With raster image correlation spectroscopy, I measure the diffusion of histones in the nucleoplasm and heterochromatin domains of the nuclei of early mouse embryos. The lower diffusion coefficient of histones in the heterochromatin domain supports the conclusion that heterochromatin forms a liquid phase-separated domain. The wide range of topics covered in this thesis demonstrate that fluorescence microscopy is more than just an imaging tool but also a powerful instrument for the quantification and elucidation of dynamic cellular processes

    A guide to designing photocontrol in proteins: methods, strategies and applications

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    Light is essential for various biochemical processes in all domains of life. In its presence certain proteins inside a cell are excited, which either stimulates or inhibits subsequent cellular processes. The artificial photocontrol of specifically proteins is of growing interest for the investigation of scientific questions on the organismal, cellular and molecular level as well as for the development of medicinal drugs or biocatalytic tools. For the targeted design of photocontrol in proteins, three major methods have been developed over the last decades, which employ either chemical engineering of small-molecule photosensitive effectors (photopharmacology), incorporation of photoactive non-canonical amino acids by genetic code expansion (photoxenoprotein engineering), or fusion with photoreactive biological modules (hybrid protein optogenetics). This review compares the different methods as well as their strategies and current applications for the light-regulation of proteins and provides background information useful for the implementation of each technique

    Cis-Regulation of Gremlin1 Expression during Mouse Limb Bud Development and its Diversification during Vertebrate Evolution

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    Embryonic development and organogenesis rely on tightly controlled gene expression, which is achieved by cis-regulatory modules (CRMs) interacting with distinct transcription factors (TFs) that control spatio-temporal and tissue-specific gene expression. During organogenesis, gene regulatory networks (GRNs) with selfregulatory feedback properties coordinately control growth and patterning and provide systemic robustness against genetic and/or environmental perturbations. During limb bud development, various interlinked GRNs control outgrowth and patterning along all three limb axes. A paradigm network is the epithelial-mesenchymal (e-m) SHH/GREM1/AER-FGF feedback signaling system which controls limb bud outgrowth and digit patterning. The BMP antagonist GREMLIN1 (GREM1) is central to this e-m interactions as its antagonism of BMP activity is essential to maintain both AER-Fgf and Shh expression. In turn, SHH signaling upregulates Grem1 expression, which results in establishment of a self-regulatory signaling network. One previous study provided evidence that several CRMs could regulate Grem1 expression during limb bud development. However, the cis-regulatory logics underlying the spatio-temporal regulation of the Grem1 expression dynamics remained obscure. From an evolutionary point of view, diversification of CRMs can result in diversification of gene regulation which can drive the establishment of morphological novelties and adaptions. This was evidenced by the observed differences in Grem1 expression in different species that correlates with the evolutionary plasticity of tetrapod digit patterning. Hence, a better understanding of spatio-temporal regulation of the Grem1 expression dynamics and underlying cis-regulatory logic is of interest from both adevelopmental and an evolutionary perspective. Recently, multiple candidate CRMs have been identified that might be functionally relevant for Grem1 expression during mouse limb bud development. For my PhD project, I genetically analyzed which of these CRMs are involved in the regulation of the spatial-temporal Grem1 expression dynamics in limb buds. Therefore, we generated various single and compound CRM mutant alleles using CRISPR/Cas9. Our CRMs allelic series revealed a complex Grem1 cis-regulation among a minimum of six CRMs, where a subset of CRMs regulates Grem1 transcript levels in an additive manner. Surprisingly, phenotypic robustness depends not on threshold transcript levels but the spatial integrity of the Grem1 expression domain. In particular, interactions among five CRMs control the characteristic asymmetrical and posteriorly biased Grem1 expression in mouse limb buds. Our results provide an example of how multiple seemingly redundant limb-specific CRMs provide phenotypical robustness by cooperative/synergistic regulation of the spatial Grem1 expression dynamics. Three CRMs are conserved along the phylogeny of extant vertebrates with paired appendages. Of those, the activities of two CRMs recapitulate the major spatiotemporal aspects of Grem1 expression in mouse limb buds. In order to study their functions in species-specific regulation of Grem1 expression and their functional diversification in tetrapods, I tested the orthologous of both CRMs from representative species using LacZ reporter assays in transgenic mice, in comparison to the endogenous Grem1 expression in limb buds of the species of origin. Surprisingly, the activities of CRM orthologues display high evolutionary plasticity, which correlates better with the Grem1 expression pattern in limb buds of the species of origin than its mouse orthologue. This differential responsiveness to the GRNs in mouse suggests that TF binding site alterations in CRMs could underlie the spatial diversification of Grem1 in limb buds during tetrapod evolution. While the fish fin and tetrapod limb share some homologies of proximal bones, the autopod is a neomorphic feature of tetrapods. The Grem1 requirement for digit patterning and conserved expression in fin buds prompted us to assess the enhancer activity of fish CRM orthologues in transgenic mice. Surprisingly, all tested fish CRMs are active in the mouse autopod primordia providing strong evidence that Grem1 CRMs are active in fin buds and that they predate the fin-to-limb transition. Our results corroborate increasing evidence that CRMs governing autopodial gene expression have been co-opted during the emergence of tetrapod autopod. Furthermore, as part of a collaboration with Dr. S. Jhanwar, I contributed to the study of shared and species-specific epigenomic and genomic variations during mouse and chicken limb bud development. In this analysis, Dr. S. Jhanwar identified putative enhancers that show higher chicken-specific sequence turnover rates in comparison to their mouse orthologues, which defines them as so-called chicken accelerated regions (CARs). Here, I analyzed the CAR activities in comparison to their mouse orthologues by transgenic LacZ reporter assays, which was complemented by analysis of the endogenous gene expression in limb buds of both species. This analysis indicates that diversified activity of CARs and their mouse orthologues could be linked to the differential gene expression patterns in limb buds of both species

    Investigating and targeting folate metabolism in chronic myeloid leukaemia

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    Chronic myeloid leukaemia (CML) is myeloproliferative disease that arises due to the formation of the fusion oncogene, BCR-ABL, in a haematopoietic stem cell (HSC). BCR-ABL oncogene has a constitutive tyrosine kinase activity and drives myeloid expansion and accumulation of mature blood cells. Due to the introduction of imatinib, a specific tyrosine kinase inhibitor (TKI), treatment of the disease has drastically improved the last 20 years. Nevertheless, imatinib and second/third generation TKIs do not eradicate leukaemic stem cells (LSCs), This population of cells persists and is therapy resistant. Thus, current research is focused on the identification of novel targets to target CML LSCs and ultimately, cure the disease. Recent work from our lab has demonstrated that CML LSCs have high mitochondrial mass and activity and that they rely on oxidative phosphorylation (OXPHOS) when compared to patient matched progenitor cells or normal HSCs. Folate-mediated one carbon (1C) metabolism plays a crucial role in nucleotide synthesis, energy homeostasis and redox defence. Furthermore, folate metabolism is a metabolic vulnerability for various haematological malignancies. As the role of folate metabolism in CML remains unknown, we aimed to investigate the importance of 1C metabolism in CML as a model of LSC-driven haematological malignancies. By using publicly available microarray datasets we established that folate metabolism associated genes are upregulated in LSCs compared to normal counterparts. Furthermore, we discovered that expression of those genes does not change in CML stem cell enriched (CD34+) primary cells following a 7-day imatinib treatment. In addition, we revealed that the activity of the pathway is significantly upregulated in CML CD34+ cells when compared to normal CD34+ cells. We also uncovered that genetic inhibition of the pathway following loss of the mitochondrial serine hydroxymethyl transferase 2 (SHMT2), leads to decreased cell growth and cell cycle arrest of K562 cells. Furthermore, loss of SHMT2 significantly impaired tumour xenograft formation of KCL22 cells. Metabolically, genetic or pharmacological (SHIN1; SHMT1/2 inhibitor) inhibition of 1C metabolism resulted in decreased de novo purine synthesis and glycolysis. Besides this, inhibition of the folate pathway led to AMPK activation, mTORC1 suppression and autophagy induction. Inhibition of the pathway also altered mitochondrial homeostasis by decreasing mitochondrial reactive oxygen species (ROS), causing hyperpolarisation of the mitochondrial membrane and accumulation of the mitochondrial fission related protein DRP1 and mitophagy receptor NIX. Of note, formate (1C unit donor independent from SHMT1/2 activity) supplementation was sufficient to reverse changes caused by folate metabolism inhibition. Phenotypically, inhibition of 1C metabolism induced the expression of erythropoiesis markers CD71 and Glycophorin A in K562 cells, which was reversed following formate supplementation. Similar results were obtained in CML CD34+ cells when challenged with erythropoietin (EPO). Furthermore, we uncovered that promotion of maturation of CML cells was AMPK-independent, but autophagy dependent. Of clinical relevance, pharmacological inhibition of 1C metabolism resulted in impaired cell proliferation and short-term colony formation potential of CML CD34+ cells, with minimum effect on normal counterparts. Lastly, combination treatment of SHIN1 with imatinib significantly increased the sensitivity of primary CML cells to imatinib. In conclusion, these results describe a novel role of folate metabolism in CML, indicating that the pathway can be a metabolic vulnerability for LSCs and sensitise this population to traditional therapeutic approaches

    Machine learning and large scale cancer omic data: decoding the biological mechanisms underpinning cancer

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    Many of the mechanisms underpinning cancer risk and tumorigenesis are still not fully understood. However, the next-generation sequencing revolution and the rapid advances in big data analytics allow us to study cells and complex phenotypes at unprecedented depth and breadth. While experimental and clinical data are still fundamental to validate findings and confirm hypotheses, computational biology is key for the analysis of system- and population-level data for detection of hidden patterns and the generation of testable hypotheses. In this work, I tackle two main questions regarding cancer risk and tumorigenesis that require novel computational methods for the analysis of system-level omic data. First, I focused on how frequent, low-penetrance inherited variants modulate cancer risk in the broader population. Genome-Wide Association Studies (GWAS) have shown that Single Nucleotide Polymorphisms (SNP) contribute to cancer risk with multiple subtle effects, but they are still failing to give further insight into their synergistic effects. I developed a novel hierarchical Bayesian regression model, BAGHERA, to estimate heritability at the gene-level from GWAS summary statistics. I then used BAGHERA to analyse data from 38 malignancies in the UK Biobank. I showed that genes with high heritable risk are involved in key processes associated with cancer and are often localised in genes that are somatically mutated drivers. Heritability, like many other omics analysis methods, study the effects of DNA variants on single genes in isolation. However, we know that most biological processes require the interplay of multiple genes and we often lack a broad perspective on them. For the second part of this thesis, I then worked on the integration of Protein-Protein Interaction (PPI) graphs and omics data, which bridges this gap and recapitulates these interactions at a system level. First, I developed a modular and scalable Python package, PyGNA, that enables robust statistical testing of genesets' topological properties. PyGNA complements the literature with a tool that can be routinely introduced in bioinformatics automated pipelines. With PyGNA I processed multiple genesets obtained from genomics and transcriptomics data. However, topological properties alone have proven to be insufficient to fully characterise complex phenotypes. Therefore, I focused on a model that allows to combine topological and functional data to detect multiple communities associated with a phenotype. Detecting cancer-specific submodules is still an open problem, but it has the potential to elucidate mechanisms detectable only by integrating multi-omics data. Building on the recent advances in Graph Neural Networks (GNN), I present a supervised geometric deep learning model that combines GNNs and Stochastic Block Models (SBM). The model is able to learn multiple graph-aware representations, as multiple joint SBMs, of the attributed network, accounting for nodes participating in multiple processes. The simultaneous estimation of structure and function provides an interpretable picture of how genes interact in specific conditions and it allows to detect novel putative pathways associated with cancer

    Engineering Tools to Probe and Manipulate the Immune System at Single-Cell Resolution

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    My thesis focuses on developing experimental and computational tools to probe and manipulate cellular transcriptomes in the context of human health and disease. Chapter 1 and 2 focus on published work where we leverage single-cell RNA sequencing (scRNA-seq) to understand human immune variability, characterize cell-type specific biases of multiple viral variants within an animal, and assess temporal immune response in the brain to delivery of genetic cargo via an adeno-associated virus (AAV). Chapter 3 and 4 present progress I have made on tools for exporting RNA extracellularly and engineering of a transcription factor for modulating macrophage state. For probing cellular transcriptome states, we have developed a platform using multiplexed single-cell sequencing and out-of-clinic capillary blood extraction to understand temporal and inter-individual variability of gene expression within immune cell types. Our platform enables simplified, cost-effective profiling of the human immune system across subjects and time at single-cell resolution. To demonstrate the power of our platform, we performed a three day time-of-day study of four healthy individuals, generating gene expression data for 24,087 cells across 22 samples. We detected genes with cell type-specific time-of-day expression and identified robust genes and pathways particular to each individual, all of which could have been missed if analyzed with bulk RNA-sequencing. Also, using scRNA-seq, we have developed a method to screen and characterize cellular tropism of multiple AAV variants. Additionally, I have looked at AAV-mediated transcriptomic changes in animals injected with AAV-PHP.eB three days and twenty-five days post-injection. I have found that there is an upregulation of genes involved in p53 signaling in endothelial cells three days post-injection. In the context of manipulating cellular transcriptomic states, I demonstrate that a fusion between RNA targeting enzyme, dCas13, and capsid-forming neuronal protein, Arc, is able to form a capsid-like structure capable of encapsulating RNA. I also present methods and preliminary data for tuning macrophage states through mutations in transcription factor EB (TFEB) using scRNA-seq as a readout.</p

    Can transposon directed insertion-site sequencing be used to predict possible outcomes of evolution?

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    Laboratory-based evolution has become a tool that is widely used to understand an organism’s response to stressful environments through linking the genotype to the phenotype. Within laboratory evolution, the role that loss of function mutations play in adaptation is a topic of debate, with recent observations suggesting that adaptive loss of function mutations are a common adaptive strategy. One limiting factor of this technique is that the time taken to conduct a single experiment can be extensive. With these points in mind, we proposed to see if a short term selection experiment on a high density transposon library, using Transposon Directed Insertion-site Sequencing (TraDIS) to analyse the data, would produce results which correlate with those from long-term evolution experiments. Since TraDIS provides a measure of relative contributions to fitness of each gene, in principle it should be possible to use TraDIS to identify genes whose loss of function provides a fitness benefit on a significantly shorter timescale. Previously in our laboratory, five populations of E. coli K-12 MG1655 were evolved in a dynamic pH environment by daily passaging over five months in unbuffered LB, starting at pH 4.5. Whole genome resequencing of the final populations and clones revealed many striking similarities in the evolutionary trajectories of these populations. Therefore, to explore the hypothesis that short term selection of a high density transposon library could identify genes that were also found in the five month evolution experiment, an E. coli K-12 MG1655 transposon library was constructed and passaged for 10 days under similar conditions as the evolution experiment at both pH 4.5 and pH 7. TraDIS analysis showed that, within these populations, insertions in a few genes had accumulated, suggesting there was a fitness advantage for a strain carrying these insertions. These genes showed a significant overlap with the ones identified in the evolution experiment. These results highlight a possible alternative approach to laboratory evolution when attempting to understand an organism’s response to stress, providing a foundation for future work to explore different conditions. Research data supporting this thesis can be found on the University of Birmingham eData repository at: https://doi.org/10.25500/edata.bham.0000075
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