Anthony Abraham


Last Name

Abraham

First Name

Anthony

ORCID

0000-0002-8710-1351

Organisational unit

Filters

2021 - 20232
Reset filters

Search Results

Publications 1 - 2 of 2
  • An AAV gene therapy computes over multiple cellular inputs to enable precise targeting of multifocal hepatocellular carcinoma in mice
    Item type: Journal Article
    Angelici, Bartolomeo; Shen, Linling; Schreiber, Jörg; et al. (2021)
    Science Translational Medicine
    Clinical translation of multi-input biomolecular computing systems holds potential to lead to disease-tailored, data-driven rational design of next-generation therapeutic modalities. However, practical demonstrations of this potential are lacking. Here, we developed a clinically translatable approach for the design and implementation of therapeutic agents comprising biomolecular multi-input logic modules for precision cell targeting, compatible with adeno-associated virus (AAV) vectors. We used this approach to engineer an AAV-encoded gene therapy prototype that, when delivered systemically, successfully treated hepatocellular carcinoma in an orthotopic mouse tumor model. The therapy performed a molecular-scale computation over multiple transcriptional and microRNA inputs based on the differential molecular profiles of tumor and nontumor cells, to guide the activation of a herpes simplex virus thymidine kinase (HSV-TK) effector gene. Multi-input computation in individual cells was necessary and sufficient to drive in vivo and in situ tumor-specific expression of HSV-TK with minimal concomitant expression in nontumor liver and other organs. Intravenous vector injection in combination with ganciclovir resulted in marked reduction in tumor burden in treated mice compared with controls, without negative effects on general well-being or weight. The therapeutic approach has the capacity to perform logical integration of diseased and healthy cell-specific molecular inputs to precisely regulate therapeutic effector gene expression and is a promising avenue for the next generation of cancer therapies. Moreover, our systematic data-driven workflow illustrates how gene expression data can shape the molecular composition of future therapeutic candidates.
  • Design and evaluation of synthetic gene circuit elements for the classification of cellular senescence
    Item type: Doctoral Thesis
    Abraham, Anthony (2023)
    Chronological ageing represents the greatest risk factor for most chronic diseases and is also directly linked to physiological decline in humans. This implies that the processes which progressively drive ageing must promote the diseases associated with it. As life expectancy increases in the modern world, so can we expect an upsurge in the number of age-related diseases. Cellular senescence is considered one of the nine hallmarks of ageing, and the accumulation of senescent cells in organs and tissues with age is known to disrupt tissue function and drive many diseases of ageing. Strategies to eliminate, impair, or reprogram senescent cells have shown great potential in improving overall health and lifespan. However, many of these therapies that target senescence have relied on a single biomarker and thus lack specificity, which can cause off-target effects. The field of synthetic biology has consistently developed in the past few decades and now describes many novel ways to manipulate the biology of cells, perform computations with biological components, and classify particular cell types. In this study, I leveraged synthetic biology components to design a gene circuit to classify senescent cells based on the three characteristics common to most senescent cells: cell cycle arrest, a bioactive secretome, and apoptosis resistance. To deliver this circuit to non-dividing senescent cells, I first assessed and then optimised the common self-inactivating lentiviral vector for the delivery of my gene circuit elements to cells. To build the three sensor parts of this gene circuit, I used RNA-sequencing (RNA-seq) and miRNA sequencing (miRNA-seq) to identify potential sensor inputs. From RNA-seq data, I devised a systematic workflow to construct and test promoter-based state sensors that should recapitulate gene expression behaviour. This yielded a sensor for cell cycle arrest but not for the other two senescence features. I then leveraged the optimised lentiviral vector to develop a platform for faithfully testing miRNA-based sensors in virus-transduced cells. With this done, I used the platform to assess a panel of miRNAs chosen from miRNA-seq data and identified a senescence-specific miRNA linked to apoptosis resistance. The results of this research provide an enhanced method of delivering gene circuit elements to non-dividing cells and identify two of the three markers necessary to construct a synthetic classifier gene circuit that can specifically identify senescent cells.
Publications 1 - 2 of 2