MICROBIOME

Disturbance of the natural microbiome is a contemporary health topic both in human and veterinary medicine. The intestinal microbiome is defined as the collection of all living microorganisms (bacteria, fungi, protozoa, and viruses) that occupy the gastrointestinal tract. With the development of new molecular techniques (such as bacterial 16S rRNA gene sequencing), it is currently recognised that the microbiome is highly diverse, containing several hundred to over a thousand bacterial phylotypes¹, and it plays an important role in the development and regulation of the host immune system, but also in the pathogenesis of multiple medical conditions².

ANTIBIOTIC USE

The administration of antibiotics can lead to the appearance of resistant bacteria either to a specific antibiotic or potentially others within the same or a different group. As a result, the number of antibiotics that are effective to treat a certain disease may become limited or even non-existent. Moreover, this resistance is not restricted to bacteria that cause disease as it may also develop in non-harmful bacteria that are exposed to the antibiotic, such as the intestinal microbiome (as represented in the faecal microbiota). In companion animals, this is of particular concern due to the frequent direct contact between people and their pets and their often-shared living space. And it is not known if the changes induced by antibiotics persist, and for how long, following completion of antibiotic therapy.

If the intestinal microbiome is altered, dysbiosis occurs. Antibiotic treatment is an important cause of dysbiosis because it can radically alter the composition of the intestinal microbiome³ and increase the risk of developing infections⁴. Since the intestinal microbiome plays a crucial role in immunity, metabolism and endocrinology, the effects of antibiotics on the faecal microbiota may lead to further health complications².

OBJECTIVES

The aims of this study are to determine if administration of antibiotics to dogs causes an effect on the overall faecal bacterial population (faecal microbiota) and/or induces antimicrobial resistance (AMR) in the faecal bacterial microbiota.

METHODOLOGY

Faecal samples will be collected from dogs prior to the oral administration of amoxycillin-clavulanate or a fluoroquinolone. Both antibiotics are defined by the World Health Organisation (WHO) as critically important and of highest priority for human medicine. Part of the fresh faecal sample will be submitted for routine culture of Escherichia coli (used as an indicator organism) and susceptibility testing. The remaining faecal material will be aliquoted and stored at -80^oC for microbiome analysis. Faecal samples will also be collected one to two weeks after discontinuation of therapy. Microbiota composition profiling of the canine faecal population will be performed by 16S rRNA amplicon sequencing as well as culture and susceptibility testing.

The emergence of AMR is considered one of the major threats to human and animal health. The results of this study will be important in determining the degree to which antibiotic administration perturbs the canine faecal microbiome and to what extent this promotes AMR.

Selected references:

1. Handl S, Dowd SE, Garcia-Mazcorro JF, et al (2011) Massive parallel 16S rRNA gene pyrosequencing reveals highly diverse faecal bacterial and fungal communities in healthy dogs and cats. FEMS Microbiology and Ecology 76:301-310
2. Kelly et al (2019) Gut Check Time: Antibiotic Delivery Strategies to Reduce Antimicrobial Resistance, Trends in Biotechnology doi.org/10.1016/j.tibtech.2019.10.008
3. Panda S, Khader IE, Casellas F, Vivancos JF, Cors MG et al (2014) Short-term effect of antibiotics on human gut microbiota. PLoS One 18;9(4): e95476. doi: 10.1371/journal.pone.0095476.
4. Tizard IR, Jones SW. The Microbiota Regulates Immunity and Immunologic Diseases in Dogs and Cats. Vet Clin North Am Small Anim Pract. 2018 Mar;48(2):307-322. doi: 10.1016/j.cvsm.2017.10.008. Epub 2017 Nov 29. PMID: 29198905.

Biophotonics

In collaboration with the Bioimaging Core facility at the UCD Conway Institute, I am engaged in biophotonics research where a range of advanced microscopic tools is used to investigate molecular phenomena occurring at a nano-bio interface. The microscopic platforms used are epifluorescence, confocal laser scanning microscopy (CLSM), and fluorescence lifetime imaging microscopy (FLIM). Recently, my group has established optimised protocols to image and analyse isolated tissue specimens based on autofluorescence (see Figure 2). Utilising autofluorescence ensures minimal tissue manipulation and excludes the use of external dyes known to interfere with the native tissue fabric. Due to the high sensitivity of the technique, it can detect subtle changes within tissue samples. Interestingly, such protocols are quite useful in detecting nascent changes within tissue blocs, often prepared from a biopsied material, due to cancer onset. The ability to detect cancer at such an early stage, often termed carcinoma in situ (CIS), is crucial as early detection holds the key in managing cancer patients, such as improving prognosis and five-year survival rates. I am now collaborating with experts in the field to develop an integrated, robust, and automated imaging modality that will obviate the variation due to an empirical interpretation of histopathological tissue samples.

Quantitative structure-activity relationship (QSAR)

Molecular chemistry determines its reactivity and how a molecule interacts under physiological conditions, including its interaction with various receptors in animal bodies (see Figure 3). An exciting class of in silico tools has now appeared to analyse both the 2D and 3D structure of a given molecule and provide a broad range of molecular descriptors as numerical readouts. These numerical databases can be analysed with various modeling programs with a range of clustering and prediction protocols plugged into the platform. These sophisticated tools can be used to design drug molecules, screen large databases of drugs based on a molecular backbone or understand the molecular mechanisms of how therapeutic molecules interact with receptors at a granular level. Such artificial intelligence (AI)-based suites are currently being used to develop molecular templates that will help synthesise drugs with precise characteristics and tunable affinity toward a receptor of choice. Moreover, in collaboration with leading experts in the field, I am currently working toward the simulation of functionalized nanoparticulate surfaces with further probation of how the surface of engineered nanoparticles interacts with various glycoproteins, including gut mucin. The obtained data will be crucial toward designing nanotherapeutic agents with tailor-made properties and prioritise the functional groups for grafting on nanoparticles to facilitate cellular delivery of pharmaceutics and genetic materials.