fluorescence-lifetime-macro-imager-for-biomedical-applications

Fluorescence Lifetime Macro Imager for Biomedical Applications

This paper presents a new photoluminescence lifetime imager designed to map the molecular oxygen (O2) concentration in different phosphorescent samples ...

University College Cork

Systemic administration of the highly potent anticancer therapeutic, tumour necrosis factor alpha (TNFα) induces high levels of toxicity and is responsible for serious side effects. Consequently, tumour targeting is required in order to confine this toxicity within the locality of the tumour. Bacteria have a natural capacity to grow within tumours and deliver therapeutic molecules in a controlled fashion. The non-pathogenic E. coli strain MG1655 was investigated as a tumour targeting system in order to produce TNFα specifically within murine tumours. In vivo bioluminescence imaging studies and ex vivo immunofluorescence analysis demonstrated rapid targeting dynamics and prolonged survival, replication and spread of this bacterial platform within tumours. An engineered TNFα producing construct deployed in mouse models via either intra-tumoural (i.t.) or intravenous (i.v.) administration facilitated robust TNFα production, as evidenced by ELISA of tumour extracts. Tumour growth was impeded in three subcutaneous murine tumour models (CT26 colon, RENCA renal, and TRAMP prostate) as evidenced by tumour volume and survival analyses. A pattern of pro-inflammatory cytokine induction was observed in tumours of treated mice vs.

Intratumoural production of TNFα by bacteria mediates cancer therapy.

Protein engineering and synthetic biology stand to benefit immensely from recent advances in silico tools for structural and functional analyses of proteins. In the context of designing novel proteins, current in silico tools inform the user on individual parameters of a query protein, with output scores/metrics unique to each parameter. In reality, proteins feature multiple "parts"/functions and modification of a protein aimed at altering a given part, typically has collateral impact on other protein parts. A system for prediction of the combined effect of design parameters on the overall performance of the final protein does not exist. Function2Form Bridge (F2F-Bridge) attempts to address this by combining the scores of different design parameters pertaining to the protein being analyzed into a single easily interpreted output describing overall performance. The strategy comprises of (a) a mathematical strategy combining data from a myriad of in silico tools into an OP-score (a singular score informing on a user-defined overall performance) and (b) the F2F Plot, a graphical means of informing the wetlab biologist holistically on designed construct suitability in the context of multiple parameters, highlighting scope for improvement. F2F predictive output was compared with wetlab data from a range of synthetic proteins designed, built, and tested for this study. Statistical/machine learning approaches for predicting overall performance, for use alongside the F2F plot, were also examined. Comparisons between wetlab performance and F2F predictions demonstrated close and reliable correlations. This user-friendly strategy represents a pivotal enabler in increasing the accessibility of synthetic protein building and de novo protein design.

Function2Form Bridge-Toward synthetic protein holistic performance prediction.

Bacterial inhabitants of the body have the potential to play a role in various stages of cancer initiation, progression, and treatment. These bacteria may be distal to the primary tumour, such as gut microbiota, or local to the tissue, before or after tumour growth. Breast cancer is well studied in this context. Amongst breast cancer types, Triple Negative Breast Cancer (TNBC) is more aggressive, has fewer treatment options than receptor-positive breast cancers, has an overall worse prognosis and higher rates of reoccurrence. Thus, an in-depth understanding of the bacterial influence on TNBC progression and treatment is of high value. In this regard, the Gut Microbiota (GM) can be involved in various stages of tumour progression. It may suppress or promote carcinogenesis through the release of carcinogenic metabolites, sustenance of proinflammatory environments and/or the promotion of epigenetic changes in our genome. It can also mediate metastasis and reoccurrence through interactions with the immune system and has been recently shown to influence chemo-, radio-, and immune-therapies. Furthermore, bacteria have also been found to reside in normal and malignant breast tissue. Several studies have now described the breast and breast tumour microbiome, with the tumour microbiota of TNBC having the least taxonomic diversity among all breast cancer types. Here, specific conditions of the tumour microenvironment (TME) - low O2, leaky vasculature and immune suppression - are supportive of tumour selective bacterial growth. This innate bacterial ability could enable their use as delivery agents for various therapeutics or as diagnostics. This review aims to examine the current knowledge on bacterial relevance to TNBC and potential uses while examining some of the remaining unanswered questions regarding mechanisms underpinning observed effects.

Ciaran Devoy

Cancer Research@UCC

University College Cork

Fluorescence Lifetime Macro Imager for Biomedical Applications

Ciaran Devoy

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University College Cork

Fluorescence Lifetime Macro Imager for Biomedical Applications

Cancer Research@UCC