Periodic Reporting for period 1 - ARCS (Elucidating the gene exchange networks of antibiotic resistance genes in clinical sewage microbiomes)
Reporting period: 2019-08-01 to 2021-07-31
"Antibiotics are essential for treating bacterial infections. However, antimicrobial resistance (AMR) has spread among pathogenic bacteria, leading to therapy failure or deaths of patients. Due to their association with promiscuous mobile genetic elements, clinically important antibiotic resistance genes (ARGs) can spread rapidly among diverse bacteria through horizontal gene transfer (HGT). Due to HGT, identifying the microbial hosts of the ARGs in an ecosystem is a challenge. Importantly, it has been shown that ARGS can spread from human gut commensals to clinical pathogens and vice versa.
Wastewater from clinical sources can importantly select for HGT of ARGs between different microbes, as both antibiotics and ARGs are present at high levels. The aim of this project - ""ARCS"" (Antibiotic Resistance in Clinical Sewage microbiomes), is to reveal the ARGs exchange networks in clinical sewage microbiomes. The research objectives are: 1) Link ARGs to microbial hosts by using novel culture-independent methods based on DNA high-throughput sequencing. 2) Link ARGs to mobile genetic elements by isolating, sequencing, and characterizing the genomes of the identified hosts of ARGs. 3) Evaluate whether the isolates can transfer their ARGs to clinical pathogens using conjugation assays. ARGS will thus lead to a step-change in our understanding of HGT of ARGs in complex microbial ecosystems.
The Fellow Dr Lisandra Zepeda's expertise in generating and analysing DNA sequencing data and the host Prof. Willem van Schaik's international leadership in AMR ensure successful completion of ARCS and a favourable knowledge transfer for both parties. Furthermore, the supportive environment of the University of Birmingham ensures the accomplishment of the Fellow's training in crucial areas such as microbial DNA capture sequencing, microbiology laboratory techniques, and mentoring skills. Thus, ARCS will enable Dr Zepeda to realise her career goal of becoming a research group leader."
Wastewater from clinical sources can importantly select for HGT of ARGs between different microbes, as both antibiotics and ARGs are present at high levels. The aim of this project - ""ARCS"" (Antibiotic Resistance in Clinical Sewage microbiomes), is to reveal the ARGs exchange networks in clinical sewage microbiomes. The research objectives are: 1) Link ARGs to microbial hosts by using novel culture-independent methods based on DNA high-throughput sequencing. 2) Link ARGs to mobile genetic elements by isolating, sequencing, and characterizing the genomes of the identified hosts of ARGs. 3) Evaluate whether the isolates can transfer their ARGs to clinical pathogens using conjugation assays. ARGS will thus lead to a step-change in our understanding of HGT of ARGs in complex microbial ecosystems.
The Fellow Dr Lisandra Zepeda's expertise in generating and analysing DNA sequencing data and the host Prof. Willem van Schaik's international leadership in AMR ensure successful completion of ARCS and a favourable knowledge transfer for both parties. Furthermore, the supportive environment of the University of Birmingham ensures the accomplishment of the Fellow's training in crucial areas such as microbial DNA capture sequencing, microbiology laboratory techniques, and mentoring skills. Thus, ARCS will enable Dr Zepeda to realise her career goal of becoming a research group leader."
"The project had to considerably change due to the following reasons. In our preliminary lab work with the sewage sample that we were provided by Dr Orsini, from University of Birmingham (UoB), we observed that we could not get as much biomass as expected. Thus, we would have needed to arrange a second visit to the sewage water cleaning site. That also meant that Dr Zepeda would have needed to try different extraction and isolation techniques in the lab. Unfortunately, all those activities had to be halted due to Covid-19. Notably, considering her asthmatic health record, it was not possible for Dr Zepeda to attend to the lab to keep receiving her laboratory training, even after the lockdown restriction measures were relaxed. Thus, Dr Zepeda modified the deliverables of the Fellowship into a more computational project.
Explanations on the modified project:
Due to the need to work from home given the lockdown restrictions and the subsequent consequences of covid-19, the Fellow started working on a purely computational project using ML and NLP techniques (for text and data mining) to find the factors driving the worldwide spread of AMR using available literature. A publication is currently being drafted, and it is at a state close to submission. The Fellow plans to submit by mid-November to the journal PLoS Computational Biology.
This project follows the innovative approach of using NLP techniques to i) develop a pipeline for the text topic classification of scientific articles, in order to obtain those AMR reports with epidemiological impact, and ii) perform feature importance and model explanatory analyses to identify the features driving the publication of reports of AMR infections by the different countries.
To perform the originally planned project, the Fellow took a training on Data Science. This training was useful for her to instead develop this alternative project during the lockdown. In regards to transfer of knowledge, the Fellow has contributed to the development of other projects in the van Schaik group, by providing useful input and feedback to her colleagues during the weekly meetings that Prof van Schaik holds with his group. Due to her data science training, she also established connections with Dr Johnson, from the School of Mathematics, UoB, who invited her to deliver a talk to the PhD math students as part of their seminar series ""Applied Mathematics"".
Also, with the help of Prof. van Schaik, she established contact with researchers from the School of Computer Science, UoB, who agreed to be her collaborators for a Fellowship application she drafted and submitted to the Royal Society (University Research Fellowship).
The Fellow was involved in performing most of the analyses of the project and in establishing collaboration with Dr Moradigaravand, Center for Computational Biology, UoB, co-author in this paper.
In more general terms, the Fellow was smoothly integrated into the host group and received significant support for the development of her research and training. Dr Zepeda held bi-weekly one-to-one meetings with her supervisor to discuss the results of her project, and she managed the financial aspects of her Fellowship with the support of the administrative staff of IMI UoB."
Explanations on the modified project:
Due to the need to work from home given the lockdown restrictions and the subsequent consequences of covid-19, the Fellow started working on a purely computational project using ML and NLP techniques (for text and data mining) to find the factors driving the worldwide spread of AMR using available literature. A publication is currently being drafted, and it is at a state close to submission. The Fellow plans to submit by mid-November to the journal PLoS Computational Biology.
This project follows the innovative approach of using NLP techniques to i) develop a pipeline for the text topic classification of scientific articles, in order to obtain those AMR reports with epidemiological impact, and ii) perform feature importance and model explanatory analyses to identify the features driving the publication of reports of AMR infections by the different countries.
To perform the originally planned project, the Fellow took a training on Data Science. This training was useful for her to instead develop this alternative project during the lockdown. In regards to transfer of knowledge, the Fellow has contributed to the development of other projects in the van Schaik group, by providing useful input and feedback to her colleagues during the weekly meetings that Prof van Schaik holds with his group. Due to her data science training, she also established connections with Dr Johnson, from the School of Mathematics, UoB, who invited her to deliver a talk to the PhD math students as part of their seminar series ""Applied Mathematics"".
Also, with the help of Prof. van Schaik, she established contact with researchers from the School of Computer Science, UoB, who agreed to be her collaborators for a Fellowship application she drafted and submitted to the Royal Society (University Research Fellowship).
The Fellow was involved in performing most of the analyses of the project and in establishing collaboration with Dr Moradigaravand, Center for Computational Biology, UoB, co-author in this paper.
In more general terms, the Fellow was smoothly integrated into the host group and received significant support for the development of her research and training. Dr Zepeda held bi-weekly one-to-one meetings with her supervisor to discuss the results of her project, and she managed the financial aspects of her Fellowship with the support of the administrative staff of IMI UoB."
"This Fellowship has had a significant and positive impact in Dr. Zepeda's career. It has allowed her to position herself as a data scientists, which massively improves her computational skills she already had as a bioinformatician. The Fellowship also gave her access to connections with other data scientists and mathematicians in the UoB, besides the experts in microbiomes and AMR in the IMI.
Her training in modelling and data science has the potential to create new market opportunities. Proof of this is her job offer as Senior Scientists in the Computational Biology Department of the company Novo Nordisk. The Fellow has accepted this offer.
The Fellow established connections with Dr. Samuel Johnson, from the School of Mathematics, UoB. Dr. Johnson is co-founder of a start-up called ""Polymaths"" has the Fellow has performed some pro-bono advisory activities, based on her Boolean modelling and NLP skills. These activities consist on discussing with Polymaths how they could use pipelines similar to the ones the Fellow developed during this Fellowship.
The original project proposal (ARCS) had clear avenues for influencing policy-making, as well as clear stakeholders. Although it was not possible to achieve the deliverables outlined in ARCS, due to covid-19, the results from the alternative developed project unveil how antibiotic consumption Is one of the main drivers of the epidemiological impact of MAR in the different countries. Such studies can be used by policymakers of the different countries to design rules limiting the use of antibiotic in certain sectors (e.g. veterinary and health). Furthermore, the developed pipeline and proof of concept can be used by the wider scientific community to be used on other topics that can exploit the large amount of available unstructured data (e.g. text)."
Her training in modelling and data science has the potential to create new market opportunities. Proof of this is her job offer as Senior Scientists in the Computational Biology Department of the company Novo Nordisk. The Fellow has accepted this offer.
The Fellow established connections with Dr. Samuel Johnson, from the School of Mathematics, UoB. Dr. Johnson is co-founder of a start-up called ""Polymaths"" has the Fellow has performed some pro-bono advisory activities, based on her Boolean modelling and NLP skills. These activities consist on discussing with Polymaths how they could use pipelines similar to the ones the Fellow developed during this Fellowship.
The original project proposal (ARCS) had clear avenues for influencing policy-making, as well as clear stakeholders. Although it was not possible to achieve the deliverables outlined in ARCS, due to covid-19, the results from the alternative developed project unveil how antibiotic consumption Is one of the main drivers of the epidemiological impact of MAR in the different countries. Such studies can be used by policymakers of the different countries to design rules limiting the use of antibiotic in certain sectors (e.g. veterinary and health). Furthermore, the developed pipeline and proof of concept can be used by the wider scientific community to be used on other topics that can exploit the large amount of available unstructured data (e.g. text)."